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+# unified_pipeline
+
+A single, self-contained synthetic signed-bank-document generator, built
+against a spec deck (`Metrics_Thresholds_Formulas` /
+`Synthetic_Test_Data_Combined.pptx`). It combines two template families --
+9 real scanned bank-document photographs with hand-measured signature-box
+annotations, and 15 procedurally (matplotlib-)drawn documents with
+cosmetic-field realism (SWIFT/NRIC/UEN/amounts) -- behind one config-driven
+(Pydantic + YAML) generation engine, a 12-case-type reject-code taxonomy,
+a shared signature dataset pool (CEDAR, SigNet, BHSig260 Hindi/Bengali), a
+richer Albumentations-backed augmentation set, and a 119-test pytest suite.
+
+**Start here if you're reviewing this:** `data/showcase_dataset/` -- 24
+pre-generated cases covering all 12 case types, each rendered once on a
+scanned template and once on a procedural template, with
+`SHOWCASE_INDEX.csv` as a one-page overview, a `before_after/` folder
+holding every case's metrics preview grouped together (see "Before/after
+degradation preview" below), and `reference_signers_index.json` mapping
+every name/role that appears in the dataset back to their signer identity
+and reference signature image(s) (see "Reference-signer index" below). No
+need to run anything to see what the pipeline produces.
+`docs/unified_pipeline_Metrics_Thresholds_Formulas.pptx` walks through the
+same metrics/thresholds/formulas with worked examples and images.
+
+## This repo is fully self-contained
+
+**Nothing here points outside this folder.** Unzip it anywhere and
+`python main.py` / `pytest` just work -- no sibling checkouts, no external
+dataset downloads. See "Datasets" below for exactly what's bundled and what
+was left out (and why).
+
+## Design notes
+
+A few structural choices worth calling out up front:
+
+- **Per-case output folders** (`case_NNN.../`) hold the rendered document,
+  the exact reference signature used (copied in, not just referenced by
+  path), and the ground truth together -- a flat `sample_NNNNNN.png/.json`
+  layout only makes sense at ML-training scale where nobody opens
+  individual cases; for a reviewed dataset, self-contained per-case folders
+  are the better fit.
+- **Ground-truth schema** is deep and fully populated (geometry/placement/
+  rendering/augmentation audit trail), with top-level convenience fields
+  (`case_id`, `expected_verdict`, `expected_reject_code`) layered on top --
+  so a reader doesn't need to know the nested `verification.verdict` shape
+  to answer "what should a verifier conclude here."
+- **Case generation** runs off one data table (`CASE_TABLE`) plus one
+  generic engine, rather than 12 separate hardcoded functions duplicating
+  the same logic.
+- **Config system** is Pydantic + YAML (validated, typed) -- catches bad
+  values before a long batch run fails partway through.
+- **Templates** are two genuinely complementary families (9 real scanned
+  photos, 15 programmatically-drawn documents) kept separate but unified
+  under one id space. `template_provider: both` is the **default** (see
+  "Template providers" below), so a plain `python main.py` shows both.
+- **Difficulty formula** is identical across both template families --
+  same weights/tiers/normalization; only the *reference constants* differ
+  (see "Difficulty calibration profiles"), and that's a config choice, not
+  a code fork.
+- **Difficulty CLI** takes a specific numeric target (`--target-difficulty
+  0.55`), not a coarse tier, since the score itself is already continuous.
+  See "Difficulty-score targeting" below.
+
+## Datasets ("use all the dataset")
+
+`SignatureDataset` (`src/signatures/signature_dataset.py`) indexes every
+source into one pool under `data/raw/`, each record tagged with
+`source_type` (genuine/forged) and `script` (latin/hindi/bengali):
+
+| Source | Path | Signer group prefix | Bundled | Notes |
+|---|---|---|---|---|
+| CEDAR | `data/raw/cedar/{genuine,forged}` | `signer_NNNN` | Full (1320 + 1320) | Used for the scanned templates. |
+| SigNet | `data/raw/signet/{train,test}` | `signet_NNNN` | Full (2149 files) | Primary Latin-script source. |
+| BHSig260-Hindi | `data/raw/bhsig260_hindi` | `bhsig260_hindi_NNNN` | Capped 8 genuine + 8 forged / writer, 160 writers (2560 files; full archive is ~8600 files / 2.3GB) | |
+| BHSig260-Bengali | `data/raw/bhsig260_bengali` | `bhsig260_bengali_NNNN` | Capped 8+8 / writer, 100 writers (1600 files; full archive is ~5400 files / 1.5GB) | |
+| GPDS | -- | `gpds_NNNN` | **Not bundled** | Never wired into the live generation path (present in the original dataset archive, never validated). `SignatureDataset` still supports it (`gpds_dir` config) if you have the archive and want to point at it. |
+
+**Dropped: an 11-file named-signer demo overlay** (`dataset/hindi_bengali/`
++ `metadata.csv`, mapping a handful of files to hand-picked names like
+"Priya Sharma"). It added no signature diversity beyond what BHSig260
+already provides at ~260x the scale (260 writers vs. 7) -- its only effect
+was a few ground-truth JSONs showing a real name instead of an
+auto-generated one. `SignatureDataset`'s `metadata.csv` support itself is
+still fully functional generic code (see `test_metadata_csv_overrides_signer_identity_and_script`
+in `tests/test_discovery.py`), just not exercised by bundled data anymore.
+
+Total bundled dataset: **~1.7GB**. Any source can be omitted by pointing
+its config path at a location that doesn't exist -- `SignatureDataset`
+skips missing sources gracefully rather than failing (this is how GPDS
+support stays in the code without being bundled).
+
+## Output structure
+
+Every generated case gets its own folder:
+
+```
+output/case_000001/
+    case_000001_document.png              # the rendered document
+    case_000001_reference_signature.png   # copy of the exact signature image used
+    case_000001_companion_signature.png   # (case types that officially test a 2nd signer)
+    case_000001_<box_id>_signature.png    # (any OTHER box on a multi-box template -- see below)
+    case_000001_visualization.png         # (--save-visualization: geometry/metrics overlay)
+    case_000001_ground_truth.json         # full metadata
+output/before_after/
+    case_000001_before_after.png          # (--save-before-after: clean vs. final side by side)
+    case_000002_before_after.png          # grouped in one folder, not scattered per-case --
+    ...                                    # easier to flip through in a row
+output/dataset_summary.csv                # one row per case: template/type/verdict/difficulty
+output/generation_summary.json            # batch totals
+output/resolved_config.yaml               # the fully-resolved config that produced this run
+```
+
+Case-type batches (`quality.case_type` set) get `_<type>` appended to the
+folder name, e.g. `case_000001_3b/`.
+
+**Every signature box on a multi-box template gets signed, always.** If a
+template has more than one signature box but the sample's case type doesn't
+officially test the extra one (e.g. `1a`/`2a`/generic samples on a 2-box
+template), that box still gets filled with its own correct, genuine
+signature rather than being left blank with no explanation -- tracked in
+`metadata["additional_signatures"]`. This never overrides a case type's own
+deliberate blank/wrong target (a `2a` case's target box still correctly
+stays blank with `NO_SIGNATURE`; only its *unrelated* second box gets the
+extra fill). See "Multi-signature-box logic, audited" below.
+
+## Template providers ("works with both template approaches")
+
+Set via `paths.template_provider` in config or `--set paths.template_provider=...`:
+
+- `both` (**default**) -- both pools merged into one id space (`bank_doc_*` + `proc_doc_*`), so a plain `python main.py` shows both template families.
+- `scanned` -- the 9 real scanned bank-document images only.
+- `procedural` -- the 15 matplotlib-drawn documents only (`proc_doc_01`..`proc_doc_15`).
+
+`paths.excluded_template_ids` (default `[]`) can pull any scanned template
+out of the pool without touching the source image file -- exclusion is
+id-stable (doesn't renumber/shift any other template's id).
+
+Every ground-truth JSON records `template_kind` (`"scanned"` / `"procedural"`)
+at the top level regardless of mode.
+
+Both families live under one `templates/` folder:
+
+```
+templates/
+    scanned/
+        guarantee/    bank_doc_01.png, bank_doc_02.png, bank_doc_03.png
+        loan_forms/   bank_doc_04.png, bank_doc_05.png, bank_doc_06.png
+        mandate/      bank_doc_07.png, bank_doc_08.png, bank_doc_09.png
+    procedural/       proc_doc_01.png .. proc_doc_15.png
+```
+
+`templates/scanned/` holds the 9 real scanned photographs (`paths.image_dir`);
+`templates/procedural/` holds the 15 procedurally-drawn documents, rendered
+once and cached as PNGs the same way a scanned template is a fixed image
+(`paths.cache_dir`) -- delete it and it's regenerated automatically on the
+next run, from the drawing code in `src/templates/procedural_template_provider.py`.
+
+## Difficulty calibration profiles
+
+Real signature ink occupies a different proportion of the page on the two
+template families, so procedural samples use different smallness/faintness/
+low_texture reference constants (`smallness_reference_area_ratio=0.003`,
+`faintness_dark_ref=165`, `faintness_light_ref=220`,
+`low_texture_reference_stddev=46` -- see
+`src/config/schema.py::DifficultyWeights`) than scanned samples, which keep
+the spec deck's literal generic values (`0.04` / `90` / `210` / `60`) --
+still an accurate fit for scanned templates' own measured population.
+
+`faintness_dark_ref` and `low_texture_reference_stddev` were refit
+2026-08-11 against a fresh 60-sample natural draw of real procedural output.
+The prior `faintness_dark_ref` (184) sat *above* the real median
+`mean_dark_intensity` (180.6), which floored `faintness` to exactly `0.000`
+for every sample measured -- a 20%-weighted component contributing nothing,
+discriminating nothing, for the whole procedural family. Re-anchored to the
+real 10th percentile (~164, rounded to 165): median sample faintness is now
+~0.29, only the darkest ~13% floor to 0, spread runs to ~0.79 for the
+lightest observed ink. `low_texture_reference_stddev` had never been split
+from the deck-literal default at all, despite the same underlying asymmetry
+existing there too (procedural's vector-rendered ink is naturally less
+textured than scanned's real-photograph ink) -- anchored to the real 90th
+percentile `gray_stddev` (~46.5, rounded to 46), the same treatment already
+given to `smallness_reference_area_ratio`. Verified: re-measured 60 fresh
+procedural samples post-fix -- faintness floors to 0 for only 8/60 (was
+60/60), low_texture floors to 0 for only 7/60; full 119-test suite still
+passes.
+
+This is applied automatically, **per sample**, by `GeneratorContext`
+(`src/generator.py::_difficulty_analyzer_for()`), keyed on each sample's own
+`template_kind()` -- not a config profile you have to remember to select.
+Previously this only applied if the whole run used
+`--config configs/procedural_templates.yaml`, which meant `template_provider:
+both` (or the plain CLI default) silently scored every procedural sample
+against the deck-literal, too-aggressive constants regardless. Fixed: each
+sample now gets the constants that actually fit its template family, even
+within a mixed `both` batch.
+
+If you explicitly set any of the 3 reference constants yourself -- a CLI
+`--set difficulty.smallness_reference_area_ratio=...` override, or a whole
+profile like `configs/procedural_templates.yaml` that sets them itself --
+that choice is respected as-is and applied to every sample regardless of
+template kind; the automatic per-kind default only fills in fields you
+didn't already decide for yourself
+(`DifficultyWeights.model_fields_set` is how this is detected).
+
+## Placement calibration
+
+Signatures are also pasted at a different scale per template family, same
+per-sample-keyed pattern as difficulty calibration above
+(`GeneratorContext._placement_engine_for()`):
+
+- **`min_signature_height_px`**: `24px -> 28px` (shared default -- see
+  `src/config/schema.py::PlacementConfig`, moved off a `placement_engine.py`
+  module constant so it's config-overridable like every other placement
+  knob). Measured 2026-08-11 across 20 real placements per candidate into a
+  genuine 18px "sign here" line: 24px -> 15.1% fully-opaque ink, 28px ->
+  16.8%, with a modest overflow cost (median 4% at 28px vs. 0% at 24px);
+  32px was tested and rejected (20%+ median overflow -- escalates much
+  faster than the legibility gain).
+- **`placement.scale_factor`**: `1.15` (procedural only; scanned keeps the
+  shipped `{min: 0.78, max: 0.95}` range unchanged). Procedural's boxes
+  (200x80px) have real headroom scanned's cramped real "sign here" lines
+  don't -- 1.1 already gave a meaningful legibility gain (opaque-ink
+  fraction 0.238 -> 0.280) with zero overflow; 1.3 starts trading further
+  gains for real overflow (6-7%). Scanned was tested too (0.9 through 1.2)
+  and showed **no change at all** in that range -- `min_signature_height_px`
+  is the actual binding constraint there, not `scale_factor`, so bumping
+  scanned's scale_factor would have been a no-op.
+
+**A real bug surfaced while implementing this**: the difficulty-calibration
+pattern detects genuine user overrides via `model_fields_set`, which works
+because `configs/default.yaml` never explicitly sets the difficulty
+reference constants. It does explicitly set `placement.scale_factor`
+though (to its own baseline range) -- so applying the same pattern
+unmodified would have silently defeated this recalibration on every single
+run, with or without a real override. Fixed by comparing the resolved
+value against the known shipped baseline directly instead of relying on
+field-presence tracking.
+
+Pasting bigger also organically fixed a gap flagged in the earlier
+recalibration work: `smallness_reference_area_ratio=0.003` previously sat
+above even the best 10% of real procedural samples (floor ~0.4, never
+reaching 0). Re-measured post-placement-fix: real median
+`signature_area_ratio` moved 0.00095 -> 0.00180, and `smallness` now
+resolves with genuine spread on its own (8/50 samples reach 0, median
+0.398) -- no further change to the reference constant was needed.
+
+## Difficulty-score targeting
+
+```bash
+python main.py --count 5 --target-difficulty 0.55
+python main.py --count 5 --target-difficulty 0.55 --difficulty-tolerance 0.03 --difficulty-max-attempts 8
+```
+
+A retry-loop (`case_generation.py::generate_case_at_difficulty()`) built on
+this pipeline's own CLI conventions (argparse, `--set key=value`):
+since the difficulty score is already a continuous `[0, 1]` value (not a
+bucket), you ask for a specific number (`0.55`), not a tier. Retries (`--difficulty-max-attempts`, default 5) with fresh but
+still fully deterministic randomness (`seed_manager.python_rng` derived
+from `f"{sample_index}_difficulty_retry{attempt}"`) until the score lands
+within `--difficulty-tolerance` (default 0.05) of the target; if no
+attempt matches, the closest one is kept and `ground_truth.json`'s
+`difficulty_targeting.matched` is `false` so this is never silently
+misreported. See `GeneratorContext.run_sample_targeting_difficulty()`.
+
+## Before/after degradation preview
+
+```bash
+python main.py --count 5 --save-before-after
+```
+
+Builds a clean-vs-final side-by-side comparison combined with a metrics
+overlay into one image. Writes
+`before_after/<case_id>_before_after.png` per sample -- grouped in one
+shared folder rather than scattered one per case folder, so every sample's
+preview can be flipped through in a row without opening each case folder
+individually:
+
+- **BEFORE (left)** -- the clean render (signature placed, before any
+  document-level augmentation), annotated with the "what was actually
+  measured" overlay: the target box (red), the actual
+  signature bbox (yellow), its detected ink polygon (green), and any
+  cosmetic field boxes (cyan = actual, orange = target cell), plus a
+  legend, a **difficulty breakdown** (`value x weight = contribution` per
+  component, e.g. `misplacement: 0.464 x 0.35 = 0.162`), and an
+  **augmentation-applied** panel listing every effect this sample rolled --
+  ON with its strength, or off.
+- **AFTER (right)** -- the final look, labeled with a one-line summary of
+  which effects actually fired (`"AFTER - 2 effect(s): contrast,
+  scanner_noise"`).
+
+Every case in `data/showcase_dataset/` has one of these pre-generated.
+Distinct from the existing `--save-visualization` (a single matplotlib-based
+multi-panel figure -- crop, ink mask, bar chart -- on the FINAL image only,
+no before/after comparison); the two can be used together. See
+`src/visualization/before_after_preview.py`.
+
+## Cosmetic (non-scored) fields
+
+`document.enable_cosmetic_fields: true` (**on by default**) fills in
+name/company-name and, on procedural templates, the fuller SWIFT/NRIC/UEN/
+amount set too -- never read by `DifficultyAnalyzer` or any scored metric.
+
+- **Procedural templates**: full field set at exact coordinates (known
+  from the drawing spec) -- see `src/cosmetics/cosmetic_fields.py`.
+- **Scanned templates**: a best-effort name/company-name field on 7 of the
+  9 templates, found via the same CV line-detection technique already used
+  for signature-box auto-detection (search a hand-picked region for the
+  blank line/table-cell, draw text against it) -- see
+  `src/cosmetics/scanned_cosmetic_fields.py`. `bank_doc_01`/`bank_doc_02`
+  are stock photos with fabricated content already baked into the image
+  (bracketed `[PLACEHOLDER ...]` text, or an already-fully-filled specimen
+  letter) and are left as-is -- there's no genuine blank field to fill
+  without inpainting over existing print.
+
+## Template exclusion (mechanism exists, nothing excluded by default)
+
+`paths.excluded_template_ids` (config list, default `[]`) can pull a
+scanned template out of the pool without touching the source image file --
+exclusion is id-stable (`TemplateLoader(excluded_ids=...)`), so pulling one
+id never renumbers/shifts any other template's id. Every discovered
+template is included by default.
+
+## Scanned cosmetic fields: field semantics, not just position
+
+`bank_doc_09`'s "Company Name" table cell (under `ENTITY DETAILS`, next to
+`Registration Number`) was pasting the *signer's own personal name* into
+it (e.g. "Nancy Ivanov" under a column literally headed "Company Name") --
+the position was correct, but the wrong *kind* of text. Every other
+scanned field really is a personal-name field ("Print Name:", "Name of
+Account Holder:", "Mr./Ms. ___") and correctly gets the signer's name;
+`company_name` is the one exception and now generates an actual company
+name (`generate_company_name()`, seeded, same generator procedural
+templates already used) instead. See `src/cosmetics/scanned_cosmetic_fields.py`'s
+`_GENERATED_FIELD_TEXT` dispatch table.
+
+### Cosmetic-field coordinates are pre-measured and cached, like signature boxes
+
+`signature_boxes` in `annotations/<template_id>.json` have always been
+hand-measured once and just read at generation time -- no per-sample image
+analysis. Cosmetic-field coordinates (where the "Name"/"Company Name" text
+gets drawn on a scanned template) now work exactly the same way, via a new
+`cosmetic_fields` array on the same annotation file:
+
+```json
+"cosmetic_fields": [
+  {"field": "company_name", "mode": "below_line", "line_x": 74, "line_y": 164, "line_w": 303}
+]
+```
+
+**How each coordinate is measured** (`scripts/detect_cosmetic_field_coordinates.py`):
+
+1. A generous *search region* is hand-picked per field, once, by eye (a
+   `(x0, y0, x1, y1)` rectangle expressed as fractions of the page's
+   width/height -- see `FIELD_REGION_RECIPES` in that script). This step
+   only needs to be roughly right, not precise.
+2. Within that region, the *same* classical-CV horizontal-line detector
+   already used to auto-detect signature boxes when no manual annotation
+   exists (`src/templates/box_detector.py`: binarize, morphological
+   opening with a wide horizontal kernel to isolate long thin dark runs,
+   merge nearby fragments) finds the actual blank underline or table
+   row-divider pixel-exactly. Deterministic given the same image, so it
+   only ever needs to run once per template.
+3. The result (`field`, `above_line`/`below_line` mode, and the detected
+   line's exact `x`/`y`/`width`) is written into the template's
+   `annotations/<id>.json`, alongside `signature_boxes`.
+
+Run it manually (`python scripts/detect_cosmetic_field_coordinates.py
+[--force]`) after adding a new scanned template or if a template image
+changes -- it is **not** run as part of normal generation. At generation
+time, `fill_scanned_cosmetic_fields()` (`src/cosmetics/scanned_cosmetic_fields.py`)
+just reads `template.annotation.pages[N].cosmetic_fields` directly, the
+same way signature-box placement already reads `signature_boxes` -- one
+measurement, reused on every sample, instead of re-running CV detection
+per call.
+
+### One "Print Name:" field per signatory, not one per template
+
+`bank_doc_03`, `04`, `05`, `06`, and `08` each have **two** signature
+boxes, and (confirmed by visually cropping every candidate line in each
+template) each box's signatory has their *own* "Print Name:"-style line --
+not one field shared by both parties. The original single-field-per-template
+recipe only ever measured one of the two, so the second signatory's name
+was always left blank regardless of what filled the first. Each field now
+carries an optional `box_id`, tying it to the `signature_boxes[].id` whose
+signer's name belongs on that line:
+
+```json
+"cosmetic_fields": [
+  {"field": "name_sig1", "mode": "above_line", "line_x": 606, "line_y": 464, "line_w": 136, "box_id": "sig1"},
+  {"field": "name_sig2", "mode": "above_line", "line_x": 828, "line_y": 464, "line_w": 136, "box_id": "sig2"}
+]
+```
+
+Fields with no `box_id` (e.g. `bank_doc_09`'s `company_name`) are
+entity-level, not tied to one signer, and are unaffected.
+
+Because a companion/additional signer's identity usually isn't resolved
+until *after* the primary box's cosmetic fields are already filled (see
+`_run_case_sample`/`_run_generic_sample` in `src/generator.py`),
+`_maybe_fill_cosmetic_fields()` only ever fills fields with no `box_id` or
+tied to the box it's given; a second pass, `_fill_box_cosmetic_field()`,
+fills the companion/extra box's own field once that signer's display name
+is known -- so both parties' names land on their own lines, matching
+whichever signature actually got pasted into their box.
+
+While narrowing `bank_doc_03`'s search region to find its second field, a
+related pre-existing bug surfaced: its *first* field had been landing on
+the "By:" line instead of "Print Name:" (both lines sit in the same
+generous search region, and `_find_line_in_region()`'s "pick the widest
+line" heuristic happened to prefer "By:"). Fixed by narrowing that
+template's region to the exact "Print Name:" line's y-band.
+
+## Legible ink on short signature lines
+
+A real "sign here" line on a scanned bank form is often only ~15-25px tall
+at this pipeline's working resolution (e.g. `bank_doc_08`'s "Signature of
+Bank Manager:" line is 22px tall) -- placing a signature stamp from a
+typical few-hundred-pixel-tall dataset source crop into a box that short
+forces a large downscale, sometimes 10-20x. `cv2.resize`'s `INTER_AREA`
+area-averages a multi-pixel-wide source stroke down into a sub-pixel-wide
+destination stroke, which is the geometrically correct way to downscale --
+but it also dilutes that stroke's alpha well below fully opaque across most
+of its footprint. A signature that was solidly inked in its source crop
+could come out mostly under 30-40% opacity: a faint gray smudge rather than
+a smaller-but-still-dark signature (real signatures squeezed onto a short
+line stay dark, just compact).
+
+`PlacementEngine.place()` (`src/placement/placement_engine.py`) now applies
+a gamma correction to the placed alpha channel after any real downscale --
+every partially-transparent pixel gets boosted non-linearly (alpha^gamma,
+gamma<1), which is what actually restores legibility (a plain linear
+rescale matching the *source's own* percentile was tried first and wasn't
+enough: even the source crop's own mid-tone pixels are already somewhat
+soft/anti-aliased from the original scan). Gamma self-scales with
+`final_scale` (the actual resize factor) -- a sample that barely shrank
+gets ~no correction (gamma near 1, alpha^1 = alpha); one squeezed 10-20x
+onto a short line gets the strongest correction (gamma floor 0.18).
+Applied to alpha only, never RGB, so ink *color* is unaffected -- this
+makes thin/faint strokes fully opaque sooner, not darker in hue. Regression
+test: `test_aggressive_downscale_onto_a_short_box_keeps_ink_dark` in
+`tests/test_placement_engine.py`.
+
+## Post-render ink detection: not fooled by the template's own printed content
+
+`GeometryAnalyzer` (`src/analysis/geometry_analyzer.py`) redetects each
+sample's signature from the rendered pixels alone (deliberately -- a real
+verifier only ever sees the final scanned document, not ground-truth
+placement coordinates). Four contamination sources were found and fixed
+across three review passes -- the first two via a user-reported case
+(`bank_doc_08`'s Bank Manager signature, a genuine PASS, whose metrics
+preview showed a bounding box/polygon spanning almost the entire printed
+line instead of just the ink), the third via a broader anomaly scan across
+the whole showcase dataset after a follow-up "the metrics still look kinda
+off" report, the fourth via the interactive UI's degradation lever
+surfacing a sample that visually looked heavily degraded yet scored "easy":
+
+1. **The printed "sign here" rule line itself.** A pasted signature very
+   often sits directly on top of, and touches, that line -- 8-connectivity
+   then merges the line and the ink into ONE component, whose bounding box
+   spans the line's full width. `_strip_printed_rule_line_pixels()` runs a
+   *narrow, absolute-width* (20px, not proportional to the crop -- a
+   signature crossing the middle of the line splits it into two shorter
+   sub-segments, and a proportional kernel could exceed either half's
+   length) horizontal morphological opening to isolate straight
+   single-row runs, then only subtracts ones whose overall connected-component
+   height stays <=3px (so a genuinely tall block of ink -- thick
+   handwriting, or a filled test fixture -- survives; only truly thin,
+   line-like structures get erased).
+2. **Printed field labels near the box** (e.g. "Bank Manager:"). A tight
+   detection pass expands slightly past the annotated box edge, and on a
+   short box that expansion can reach into adjacent label text. Fixed by
+   optionally diffing against the clean, unsigned template image
+   (`analyze(..., baseline_image_rgb=...)`) -- pixels already dark in the
+   baseline (permanent printed content) are never counted as new ink. The
+   baseline is known in advance the same way a real production pipeline
+   knows its own form layouts, not this run's specific signature
+   placement, so this isn't using ground truth the way a real verifier
+   couldn't. Originally skipped automatically when the sample underwent a
+   page-level geometric transform (`perspective`/`page_tilt`/`crumple`)
+   that would misalign a pixel-for-pixel diff -- see contamination source
+   4 below for why that gap itself needed fixing.
+
+Net effect on the reported case: `box_coverage_pct` dropped from a
+contamination-inflated 56% to an accurate ~10%, and the detected polygon
+now tightly traces just the actual ink.
+
+3. **A printed caption below a deliberately BLANK target box** (2a/5a case
+   types -- `_BLANK_CASE_TYPES`). The widened retry pass (`analyze()`'s
+   second, more generous search) exists to chase a signature that was
+   deliberately misplaced far from its own box -- but a 2a/5a target box
+   has no signature at all, so there's nothing to chase, and on a sample
+   where `baseline_image_rgb` had to be omitted (a geometric transform was
+   applied, so a pixel diff would misalign), the widened search had no
+   baseline to exclude already-printed content with. Caught a printed
+   role-caption ("1. SIGNATURE: AUTHORIZED REPRESENTATIVE *") sitting just
+   below the blank box and reported it as a wildly-misplaced signature
+   (`distance_from_box_center_px` ~90-130px, `margin_utilization` >2).
+   Fixed with `analyze(..., expect_blank=True)`, passed whenever the
+   caller already knows from ground truth that this target has no
+   signature (`target_rgba is None` in `_run_case_sample`) -- skips the
+   widened retry entirely, so a genuinely blank box just reports
+   `detected: false` and zeroed metrics, exactly as it should.
+
+4. **Whole blocks of printed text, whenever `perspective`/`page_tilt`/
+   `crumple` actually fired.** Found via the interactive UI's degradation
+   lever: a heavily rotated/noisy "Explore from clean" preview scored
+   16% ("easy") despite looking obviously harder to read than the clean
+   original (48%, "medium"). Root cause: contamination source 2's
+   baseline-diff exclusion was skipped entirely whenever a geometric
+   transform fired (a rotated page no longer lines up pixel-for-pixel with
+   the un-rotated baseline template), so the ONLY defense against printed
+   content near the box was gone -- and `_strip_printed_rule_line_pixels()`
+   only catches thin rule *lines*, not printed paragraph *text*. On one
+   measured sample, a `page_tilt` of well under 1 degree was enough for a
+   nearby paragraph heading to rotate into the search box's expanded
+   region and get counted wholesale as "ink": detected pixels jumped from
+   545 (the real signature) to 13,993, `mean_dark_intensity` dropped from
+   175 to 107 (consistent with picking up dense black print), and the
+   artificially large, well-centered "blob" read as LESS misplaced and
+   LESS small than the real, small, genuine signature -- the opposite of
+   what a harder-to-read page should score. Fixed at the source instead of
+   working around it: `AugmentationEngine.apply_to_document()` now accepts
+   an optional `baseline_image`, and whenever perspective/page_tilt/crumple
+   actually apply, the identical transform (same warp matrix / same
+   `map_x`,`map_y` remap field) is also applied to the baseline companion
+   image, so it stays pixel-aligned with the final document and baseline
+   exclusion keeps working instead of being skipped. Verified in isolation:
+   `page_tilt` alone at maximum strength now detects 552 ink pixels (vs.
+   545 with no tilt at all) -- effectively fixed.
+
+   **A separate, smaller contamination source was found (not fixed) while
+   verifying #4 above:** forcing `scanner_noise`/`salt_pepper`/`dust` on
+   simultaneously with everything else -- as the lever's "Explore from
+   clean (all effects)" mode deliberately does -- inflates detected ink
+   pixels through a *different* mechanism: the geometry pipeline scores a
+   signature using the convex hull/bounding box of *every* surviving "ink"
+   pixel, so even a few scattered noise specks near opposite corners of the
+   search box balloon that box to span nearly the whole region, regardless
+   of how little total dark area they cover (measured: ink pixel *count*
+   only roughly doubled, but IoU-with-box jumped from 0.11 to 0.82). A
+   median-blur denoise pre-pass was prototyped as a fix and rejected: a
+   kernel strong enough to meaningfully suppress noise at extreme,
+   all-effects-maxed strength (5x5) also cut a genuinely clean sample's
+   real ink-pixel count by ~60% (403 -> 157) -- real pen strokes are only a
+   few pixels wide, the same order of magnitude as the noise being
+   filtered, so the fix would trade real detection accuracy for a mostly
+   synthetic problem. A shape/cluster-aware alternative (discard ink
+   components far from the "main" cluster) was also rejected: it directly
+   conflicts with this same section's existing, deliberate design (the
+   hull is meant to *bridge* a genuine flourish far from the main strokes,
+   not discard it) and would need real testing against genuine faint/thin
+   signatures to avoid new false negatives -- a bigger investment than
+   warranted, since at realistic (non-maxed) strengths the effect is mild
+   (measured: only ~8% ink-pixel inflation at strength 0.3, vs. 25x for the
+   page_tilt bug above). Left as a documented limitation of the "Explore
+   from clean (all effects)" lever mode specifically -- which stacks all 14
+   effects simultaneously, something real generation never does (each
+   effect fires independently/probabilistically) -- rather than changing
+   detection logic; the lever now shows an explicit warning above 25% in
+   that mode instead of a silently-misleading score.
+
+## Placement floor: 28px, not the deck's 16px -- resolved by measurement, not forced
+
+A full line-by-line audit against `Metrics_Thresholds_Formulas.pptx`'s
+"Threshold reference sheet" slide found every constant in this pipeline
+matches exactly except one: `min_signature_height_px`
+(`src/config/schema.py::PlacementConfig`, formerly a
+`placement_engine.py` module constant) is 24px-then-28px in code vs. the
+deck's 16px. Rather than silently override it to match the deck, or leave
+it unexplained, this was resolved empirically: a real signature was placed
+into a genuine short 18px "sign here" line (the deck's own "real lines are
+12-20px tall" range) at each floor value and the result measured directly.
+
+| Floor | Placed height | Fully-opaque ink (alpha > 200/255) |
+|---|---|---|
+| 16px (deck) | 16px | 12.3% |
+| 24px (first pass) | 22px | 15.1% |
+| 28px (current, see "Placement calibration" above) | -- | 16.8% |
+
+Each step up measurably preserves more solid, legible ink -- `cv2.INTER_AREA`
+dilutes a multi-pixel source stroke's alpha the smaller the downscale
+target gets, and 16px sits deepest in that dilution zone (the same artifact
+already described in this file's placement-scaling comments). 32px was
+also tested (see "Placement calibration" above) and rejected -- overflow
+past 28px escalates much faster than the legibility gain. The constant's
+own definition cites this measurement directly, the same way
+`DifficultyWeights`' smallness/faintness constants document their own
+deliberate deviation from the deck's generic values.
+
+This was one finding from a full line-by-line audit (every formula/
+threshold/case-type in the deck checked against this codebase) -- 52 of 53
+checks matched exactly; this was the one documented deviation, resolved by
+measurement rather than left silently unexplained.
+
+## The ink polygon on the preview tints the real ink, not the hull
+
+Several rendering styles were tried this session, in order, each fixing
+the previous one's complaint:
+
+1. **Hull, outline only** (the original design). A convex hull "bridges"
+   gaps between separate ink blobs (letters, a flourish far from the main
+   strokes) with straight lines -- the bridging edges visibly don't hug
+   the ink wherever they cross a gap. Reported: "the polygon doesn't even
+   cover the signature."
+2. **Hull, translucent fill.** Fixed #1 (the ink is always inside the
+   tinted region), but a filled hull shades in every gap it bridges too.
+   Reported, correctly: "shouldn't [it] only cover surround the signature
+   not whitespaces."
+3. **Hull outline + `cv2.approxPolyDP` simplification** (a vertex-count
+   reduction on the hull polygon). Still visibly failed to "hug" a
+   wide/gappy signature on direct re-inspection, since it's still
+   fundamentally the same convex hull -- a known tradeoff of hull-based
+   rendering that just isn't obvious on a small, compact signature.
+4. **`display_contours`, translucent fill** (current, final). A
+   purely-visual field on `GeometryResult`: one real boundary contour per
+   disjoint ink blob, via `cv2.findContours` on the exact mask used for
+   scoring (mildly dilated first, on a copy, just to merge one letter's
+   own stroke fragments into a single blob -- nowhere near enough to
+   bridge a real gap between separate letters). Filling each blob's own
+   contour tints only actual ink, never the whitespace between strokes,
+   and -- unlike an *outline* of these same contours (tried and rejected
+   earlier: for a dense cursive/Devanagari signature it visually resembled
+   a second overlaid signature) -- a fill has no separate line art to be
+   confused with handwriting.
+
+`signature_polygon_px`/`convex_hull_px` in `ground_truth.json` remain the
+convex hull (with the `approxPolyDP` simplification from step 3 kept, as a
+minor genuine improvement to the scoring polygon's vertex count) -- every
+coverage metric (`box_coverage_pct`/IoU/`containment_pct`/`margin_utilization`)
+still comes from the hull, unchanged. `display_contours` is additive and
+explicitly documented as "never used in scoring." Both
+`before_after_preview.py` and `visualizer.py` (`--save-visualization`)
+fill `display_contours` when present, falling back to filling the hull for
+any geometry dict that predates this field. This was a deliberate choice,
+made after comparing several rendering styles directly -- prioritizing an
+accurate, non-misleading preview over hull-only visual simplicity.
+
+## Metrics preview shows every signed box, not just the scored one
+
+A multi-box template's companion/additional-signature boxes are genuinely
+rendered with real ink but aren't part of a sample's *scored* target
+(geometry/difficulty stay keyed to the one target box the rest of the
+pipeline scores) -- previously the before/after metrics preview only ever
+overlaid that one box, leaving a second signature elsewhere on the page
+unexplained. `compute_extra_signature_overlays()` (`src/generator.py`)
+re-runs `GeometryAnalyzer` (view-only, never for scoring) on every other
+signed box for the preview, and `save_before_after_preview()`'s
+`extra_boxes` param draws them in a distinct purple/pink palette, labeled
+by role (e.g. "Account Holder (other box)") instead of full re-scoring.
+
+## Reference-signer index
+
+`scripts/generate_showcase_dataset.py` also writes
+`data/showcase_dataset/reference_signers_index.json` -- every distinct
+name/role that appears anywhere in the dataset (as a primary signer,
+companion, or additional/other-box signer), with which signer identity
+backs it and where its ink actually comes from:
+
+```json
+{
+  "signer_group": "signet_0009",
+  "display_name": "John Thompson",
+  "script": "latin",
+  "roles": ["Bank Manager"],
+  "appears_in": [{"case_id": "case_001_1a_scanned", "role": "Bank Manager", "as": "primary", "template": "bank_doc_08"}],
+  "reference_images": ["data/raw/signet/train/009/009_24.PNG"]
+}
+```
+
+`signer_group` is the pipeline's internal, deterministic identity key
+(`src/verification/registry.py::SignerIdentityRegistry`) -- `display_name`
+is the human-readable name derived from it, stable across every case that
+identity appears in. `appears_in` distinguishes *how* that identity was
+used in each case (`"primary"` = the case's own target signer, `"companion"`
+= the officially-tested second signer for `1c`/`4a`/`4b`/`5a`/`5b`,
+`"additional"` = an unrelated-but-still-genuine extra box fill -- see
+"Every signature box on a multi-box template gets signed" above).
+`reference_images` collects every distinct source image path used for that
+identity across the whole dataset (not just one case), each already copied
+into its case folder too (`*_reference_signature.png` /
+`*_companion_signature.png` / `*_<box_id>_signature.png`) so nothing here
+depends on `data/raw/` actually being present to inspect a specific case.
+
+## Multi-signature-box logic, audited (and a real gap fixed)
+
+Every case type that needs two signature boxes (`1c`, `4a`, `4b`, `5a`,
+`5b` -- see `CASE_TABLE`'s `fill_companion` flag) was verified end-to-end
+across both scanned and procedural 2-box templates: template selection
+correctly restricts to templates with >=2 boxes, the companion box is
+always filled with its own correct authorized signer, and the target box
+gets exactly what its case type specifies (`1c`: also correct; `4a`: the
+*companion's* signer, in the wrong box, same form; `4b`: a signer
+authorized on a *different* template entirely; `5a`: left blank; `5b`: an
+unrelated wrong signer) -- confirmed both from the ground-truth JSON
+(`expected_signer_group` vs `actual_signer_group` vs the companion's
+group) and from rendered images.
+
+**Gap found and fixed:** case types that *don't* officially test a second
+signer (`1a`, `1b`, `1d`, `2a`, `2b`, `3a`, `3b` -- `fill_companion=False`)
+and plain generic (no `quality.case_type` at all) samples were leaving any
+*extra* box on a multi-box template completely blank, with no verdict or
+reject_code explaining why -- it just looked like a broken render. Fixed
+in both `_run_generic_sample` and `_run_case_sample`
+(`src/generator.py`): any signature box beyond the one the sample is
+actually scored/tested against now gets filled with its own correct
+authorized signer too, recorded under the new `additional_signatures`
+metadata field (list of `{box_id, role, signature_id, signer_group,
+source_type, script, ref_path}`) and copied into the case folder as
+`..._<box_id>_signature.png`. This only ever *adds* a properly-signed box
+-- it never overrides a case type's own deliberate blank/wrong-signer
+target (verified: a `2a` case's target box is still correctly blank with
+`NO_SIGNATURE`, while its unrelated second box is now properly signed
+instead of mysteriously empty).
+
+One real annotation bug found and fixed in the process: `bank_doc_09`'s
+bundled signature-box annotation (`annotations/bank_doc_09.json`,
+inherited as-is from an earlier revision) placed both boxes 27px too
+high -- inside the paragraph text above the "First/Second Authorised
+Signatory" line instead of on it. Found by rendering a sample and looking
+at where the ink actually landed (not visible from the annotation JSON
+alone), confirmed against the template's real line position via the same
+CV line-detector `scanned_cosmetic_fields.py` uses, and corrected.
+
+## A bug worth knowing about (fixed): reference signature images
+
+An earlier version of the per-case output copied the reference signature
+file with `shutil.copy2` under a `*_reference_signature.png` name. For
+CEDAR/SigNet sources (already `.png`) that's fine; for **BHSig260 sources
+(`.tif` files)** it silently produced a file containing real TIFF bytes
+under a `.png` name -- any standards-compliant image viewer correctly
+refuses to open it. Fixed by decoding with Pillow and re-saving as a real
+PNG (`src/generator.py::_export_reference_signature`) -- every reference/
+companion signature image is now guaranteed to actually be what its
+extension claims. Regression test: `tests/test_scanned_cosmetic_fields.py::test_export_reference_signature_produces_a_real_png_from_a_tiff_source`.
+
+## Usage
+
+```bash
+pip install -r requirements.txt
+
+# The flagship demo -- all 12 case types x both template families, 24 cases
+python scripts/generate_showcase_dataset.py
+# (already generated once into data/showcase_dataset/ -- rerun to regenerate)
+
+# Default: both template pools, deck-literal difficulty constants
+python main.py --count 20 --seed 1
+
+# Procedural only, recalibrated difficulty constants
+python main.py --config configs/procedural_templates.yaml --count 20 --seed 1
+
+# Scanned only
+python main.py --set paths.template_provider=scanned --count 20 --seed 1
+
+# A specific case type (see src/verification/case_types.py for all 12)
+python main.py --config configs/procedural_templates.yaml --set quality.case_type=3b --count 10
+
+# Request a specific difficulty score, with a before/after preview
+python main.py --count 5 --target-difficulty 0.55 --save-before-after
+```
+
+## Interactive UI
+
+```bash
+streamlit run streamlit_app.py
+```
+
+A local browser UI (`streamlit_app.py`) for one-off, customizable document
+creation -- pick template provider/id (picking a specific template shows a
+live preview of its blank form, with its document type and signature-box
+roles, right below the dropdown, before you even generate anything), case
+type (with its own description/verdict shown inline), a natural or
+targeted difficulty (as a plain percentage, not a raw `0.0-1.0` score), a
+**difficulty calibration** toggle (see below), signer name/role overrides,
+cosmetic fields on/off, and a seed, then generate and immediately see the
+rendered document, verdict, and a plain difficulty percentage (the
+easy/medium/hard tier is kept in the ground-truth data for case-type
+validation, but isn't the headline UI number anymore -- a caption under
+the percentage shows it for anyone who wants it), and (organized into tabs
+to keep the page scannable):
+- **Difficulty & degradation** -- what actually built the difficulty score
+  (a labeled progress bar per weighted component --
+  misplacement/whitespace/faintness/smallness/low texture, each with a
+  one-line plain-English explanation) and which degradation effects
+  actually, randomly fired for this specific sample (not just which were
+  possible). Difficulty calibration is now automatic per sample (see
+  "Difficulty calibration profiles" above) -- procedural samples get
+  `smallness_reference_area_ratio=0.003` / `faintness_dark_ref/light_ref=
+  165/220` / `low_texture_reference_stddev=46` and scanned samples keep the
+  deck-literal `0.04` / `90` / `210` / `60`, without needing to opt in. The
+  sidebar's **"Force deck-literal reference for ALL templates"** checkbox
+  (off by default) is only there to deliberately turn this OFF -- forcing
+  every sample, including procedural ones, back onto the deck-literal
+  constants that peg smallness/faintness near their ceiling for almost any
+  real signature (real signature area is only ~0.2-1.5% of the page vs. the
+  deck's 4% "normal"), e.g. to compare
+  against pre-recalibration scores.
+- **Interactive lever** -- drag a slider to watch the same clean render
+  pass through progressively stronger document degradation live, without
+  re-running the whole pipeline. Three modes: *match this document*
+  (default) scales only the effects that actually fired, together, from
+  0% (clean) to 100% (this document's real per-effect strengths -- so it
+  closely matches the Before/After tab, modulo the lever using its own
+  seed key for random-variant choices like stain shape/shadow corner);
+  *adjust individually* gives one slider per document-level effect,
+  defaulted to what actually fired (0% if it didn't), so you can push any
+  single effect harder/weaker/on independently of the rest; *explore from
+  clean* ignores what actually fired entirely and forces all 14 effects on
+  together at one shared strength, starting from clean -- a generic
+  worst-case stress-test (this is what the original version of the lever
+  always did, kept as its own mode). Both the clean and degraded preview
+  now show a live `difficulty_score`, freshly re-detected from the
+  pictured image with the same `GeometryAnalyzer`/`DifficultyAnalyzer` the
+  real pipeline uses -- it updates as you drag, but is computed
+  independently of (and can legitimately differ from) the "Difficulty"
+  badge at the top of the page, which reflects the actual generated
+  document. The lever only covers the 14 whole-page effects
+  (`apply_to_document()`) -- the 6 signature-only effects (rotation,
+  stroke thickness, ink bleed/fading, blur) run earlier, on the signature
+  snippet alone before it's pasted onto the page, so they're already baked
+  into "Clean" as plain pixels and can't be toggled here; a warning banner
+  surfaces exactly which of them fired (if any) for the current sample, so
+  the gap is visible rather than silent.
+- **Before / After (real)** -- the saved before/after preview PNG, with a
+  note that BEFORE's colored boxes are a measurement diagnostic
+  (target/detected/cosmetic-field boxes) and AFTER deliberately has no
+  overlay, since it's meant to show what a real reviewer/scanner would see.
+- **Ground truth & export** -- the full ground-truth JSON, the equivalent
+  `python main.py --set ...` CLI command (every control is a thin wrapper
+  over a config override the CLI already accepts, so anything built in the
+  UI is exactly reproducible from the command line), and "Save this case",
+  which writes the document/ground-truth/reference-signature into
+  `data/ui_generated/<name>/`, in the same per-case-folder shape as
+  `output/`/`data/showcase_dataset/`.
+
+## Tests
+
+```bash
+python -m pytest -q   # 119 passed, ~2-3 min
+```
+
+Covers formula correctness, placement, augmentation, validation, all 12
+case types, end-to-end reproducibility, `ProceduralTemplateProvider`,
+`CompositeTemplateProvider`, the additive signature sources, and the two
+difficulty-calibration profiles.
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diff --git a/unified_pipeline/data/showcase_dataset/SHOWCASE_INDEX.csv b/unified_pipeline/data/showcase_dataset/SHOWCASE_INDEX.csv
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+case_id,case_type,description,template_provider,template_id,verdict,reject_code,signer_name,signer_script,difficulty_tier,difficulty_score,validation_passed
+case_001_1a_scanned,1a,Correct signer signs normally,scanned,bank_doc_08,PASS,,John Thompson,latin,hard,0.7126,True
+case_002_1a_procedural,1a,Correct signer signs normally,procedural,proc_doc_04,PASS,,Sandra Harris,hindi,easy,0.3262,True
+case_003_1b_scanned,1b,"Correct signer, natural pen variation applied",scanned,bank_doc_09,PASS,,Nancy Ivanov,bengali,hard,0.7614,True
+case_004_1b_procedural,1b,"Correct signer, natural pen variation applied",procedural,proc_doc_04,PASS,,Donald Ali,latin,hard,0.7185,True
+case_005_1c_scanned,1c,"Multi-signer form, both sign correctly",scanned,bank_doc_04,PASS,,Patricia Baker,hindi,medium,0.523,True
+case_006_1c_procedural,1c,"Multi-signer form, both sign correctly",procedural,proc_doc_07,PASS,,Edward Williams,bengali,medium,0.6441,True
+case_007_1d_scanned,1d,"Correct signer, scan degraded to test robustness",scanned,bank_doc_09,PASS,,Hans Anderson,hindi,hard,0.7572,True
+case_008_1d_procedural,1d,"Correct signer, scan degraded to test robustness",procedural,proc_doc_15,PASS,,Scott Wright,hindi,medium,0.4664,True
+case_009_2a_scanned,2a,Box left completely blank,scanned,bank_doc_07,REJECT,NO_SIGNATURE,Scott Martinez,latin,hard,0.7592,True
+case_010_2a_procedural,2a,Box left completely blank,procedural,proc_doc_06,REJECT,NO_SIGNATURE,David Adams,bengali,hard,1.0,True
+case_011_2b_scanned,2b,"Printed/typed name only, no handwriting",scanned,bank_doc_08,REJECT,NO_SIGNATURE,Emily Davis,hindi,medium,0.4785,True
+case_012_2b_procedural,2b,"Printed/typed name only, no handwriting",procedural,proc_doc_04,REJECT,NO_SIGNATURE,Mei Young,latin,easy,0.3214,True
+case_013_3a_scanned,3a,Wrong real person signs the box,scanned,bank_doc_05,REJECT,IDENTITY_MISMATCH,Margaret Mitchell,latin,hard,0.7691,True
+case_014_3a_procedural,3a,Wrong real person signs the box,procedural,proc_doc_12,REJECT,IDENTITY_MISMATCH,Samantha Adams,bengali,easy,0.305,True
+case_015_3b_scanned,3b,Skilled-forgery sample used,scanned,bank_doc_06,REJECT,IDENTITY_MISMATCH,Linda Nguyen,hindi,hard,0.666,True
+case_016_3b_procedural,3b,Skilled-forgery sample used,procedural,proc_doc_12,REJECT,IDENTITY_MISMATCH,Fatima Lee,bengali,medium,0.5829,True
+case_017_4a_scanned,4a,"Authorized signer, but wrong role's box",scanned,bank_doc_09,REJECT,ROLE_MISMATCH,Sandra Harris,latin,medium,0.6322,True
+case_018_4a_procedural,4a,"Authorized signer, but wrong role's box",procedural,proc_doc_11,REJECT,ROLE_MISMATCH,Emily Jones,bengali,medium,0.6246,True
+case_019_4b_scanned,4b,Signer from a different form entirely,scanned,bank_doc_08,REJECT,ROLE_MISMATCH,Stephanie Martinez,hindi,hard,0.6823,True
+case_020_4b_procedural,4b,Signer from a different form entirely,procedural,proc_doc_09,REJECT,ROLE_MISMATCH,Amanda Suzuki,bengali,easy,0.2546,True
+case_021_5a_scanned,5a,Required signer's box left empty,scanned,bank_doc_05,REJECT,REQUIRED_SIGNER_ABSENT,Carlos Wilson,latin,hard,1.0,True
+case_022_5a_procedural,5a,Required signer's box left empty,procedural,proc_doc_09,REJECT,REQUIRED_SIGNER_ABSENT,Jennifer White,latin,hard,1.0,True
+case_023_5b_scanned,5b,Someone else signs in the required signer's place,scanned,bank_doc_09,REJECT,REQUIRED_SIGNER_ABSENT,Stephen King,bengali,hard,0.7202,True
+case_024_5b_procedural,5b,Someone else signs in the required signer's place,procedural,proc_doc_11,REJECT,REQUIRED_SIGNER_ABSENT,Noah Hill,latin,medium,0.629,True
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+}
\ No newline at end of file
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diff --git a/unified_pipeline/data/showcase_dataset/case_014_3a_procedural/case_014_3a_procedural_ground_truth.json b/unified_pipeline/data/showcase_dataset/case_014_3a_procedural/case_014_3a_procedural_ground_truth.json
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diff --git a/unified_pipeline/data/showcase_dataset/case_015_3b_scanned/case_015_3b_scanned_reference_signature.png b/unified_pipeline/data/showcase_dataset/case_015_3b_scanned/case_015_3b_scanned_reference_signature.png
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diff --git a/unified_pipeline/data/showcase_dataset/case_016_3b_procedural/case_016_3b_procedural_ground_truth.json b/unified_pipeline/data/showcase_dataset/case_016_3b_procedural/case_016_3b_procedural_ground_truth.json
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diff --git a/unified_pipeline/data/showcase_dataset/case_019_4b_scanned/case_019_4b_scanned_ground_truth.json b/unified_pipeline/data/showcase_dataset/case_019_4b_scanned/case_019_4b_scanned_ground_truth.json
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diff --git a/unified_pipeline/data/showcase_dataset/case_024_5b_procedural/case_024_5b_procedural_reference_signature.png b/unified_pipeline/data/showcase_dataset/case_024_5b_procedural/case_024_5b_procedural_reference_signature.png
new file mode 100644
index 0000000000000000000000000000000000000000..9464c22e7f79cfa57c250b870faf9cfb7531990d
--- /dev/null
+++ b/unified_pipeline/data/showcase_dataset/case_024_5b_procedural/case_024_5b_procedural_reference_signature.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc07f61baa38e29271def9db4381f921a1624e797da4ed42ce2255247fcb7f2d
+size 119505
diff --git a/unified_pipeline/data/showcase_dataset/reference_signers_index.json b/unified_pipeline/data/showcase_dataset/reference_signers_index.json
new file mode 100644
index 0000000000000000000000000000000000000000..ba91b5d6fe62f0dc8a8922b865b72f5f56a30a1b
--- /dev/null
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+      {
+        "case_id": "case_012_2b_procedural",
+        "role": "Primary Debtor",
+        "as": "additional",
+        "template": "proc_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/87/H-S-087-G-02.tif"
+    ]
+  },
+  {
+    "signer_group": "signet_0035",
+    "display_name": "Brian Clark",
+    "script": "latin",
+    "roles": [
+      "Bank Manager"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_019_4b_scanned",
+        "role": "Bank Manager",
+        "as": "companion",
+        "template": "bank_doc_08"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/035/10_035.png"
+    ]
+  },
+  {
+    "signer_group": "signet_0049",
+    "display_name": "Brian Ivanov",
+    "script": "latin",
+    "roles": [
+      "First Authorised Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_003_1b_scanned",
+        "role": "First Authorised Signatory",
+        "as": "additional",
+        "template": "bank_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/049/11_049.png"
+    ]
+  },
+  {
+    "signer_group": "signet_0069",
+    "display_name": "Carlos Wilson",
+    "script": "latin",
+    "roles": [
+      "Authorized Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_021_5a_scanned",
+        "role": "Authorized Signatory",
+        "as": "companion",
+        "template": "bank_doc_05"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/test/069/07_069.png"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0103",
+    "display_name": "Deborah Wright",
+    "script": "hindi",
+    "roles": [
+      "Lead Arranger Officer"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_010_2a_procedural",
+        "role": "Lead Arranger Officer",
+        "as": "additional",
+        "template": "proc_doc_06"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/103/H-S-103-G-05.tif"
+    ]
+  },
+  {
+    "signer_group": "signer_0020",
+    "display_name": "Donald Ali",
+    "script": "latin",
+    "roles": [
+      "Primary Debtor"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_004_1b_procedural",
+        "role": "Primary Debtor",
+        "as": "primary",
+        "template": "proc_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/cedar/genuine/original_20_10.png"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0076",
+    "display_name": "Edward Hernandez",
+    "script": "bengali",
+    "roles": [
+      "Account Holder"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_011_2b_scanned",
+        "role": "Account Holder",
+        "as": "additional",
+        "template": "bank_doc_08"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/76/B-S-76-G-07.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0023",
+    "display_name": "Edward Williams",
+    "script": "bengali",
+    "roles": [
+      "Chief Executive Officer"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_006_1c_procedural",
+        "role": "Chief Executive Officer",
+        "as": "primary",
+        "template": "proc_doc_07"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/23/B-S-23-G-07.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0019",
+    "display_name": "Emily Jones",
+    "script": "bengali",
+    "roles": [
+      "Board Witness Exec",
+      "Issuing Debtor"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_018_4a_procedural",
+        "role": "Board Witness Exec",
+        "as": "primary",
+        "template": "proc_doc_11"
+      },
+      {
+        "case_id": "case_018_4a_procedural",
+        "role": "Issuing Debtor",
+        "as": "companion",
+        "template": "proc_doc_11"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/19/B-S-19-G-02.tif",
+      "data/raw/bhsig260_bengali/19/B-S-19-G-04.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0051",
+    "display_name": "Fatima Lee",
+    "script": "bengali",
+    "roles": [
+      "Authorized Chief Treasurer"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_016_3b_procedural",
+        "role": "Authorized Chief Treasurer",
+        "as": "primary",
+        "template": "proc_doc_12"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/51/B-S-51-F-05.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0070",
+    "display_name": "Hans Anderson",
+    "script": "hindi",
+    "roles": [
+      "First Authorised Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_007_1d_scanned",
+        "role": "First Authorised Signatory",
+        "as": "primary",
+        "template": "bank_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/70/H-S-70-G-05.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0043",
+    "display_name": "Hiroshi Muller",
+    "script": "hindi",
+    "roles": [
+      "Account Holder"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_009_2a_scanned",
+        "role": "Account Holder",
+        "as": "additional",
+        "template": "bank_doc_07"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/43/H-S-43-G-03.tif"
+    ]
+  },
+  {
+    "signer_group": "signet_0006",
+    "display_name": "Ingrid Rossi",
+    "script": "latin",
+    "roles": [
+      "Bank Representative"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_005_1c_scanned",
+        "role": "Bank Representative",
+        "as": "companion",
+        "template": "bank_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/006/006_04.PNG"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0128",
+    "display_name": "Jason Brown",
+    "script": "hindi",
+    "roles": [
+      "Corporate Guarantor"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_004_1b_procedural",
+        "role": "Corporate Guarantor",
+        "as": "additional",
+        "template": "proc_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/128/H-S-128-G-06.tif"
+    ]
+  },
+  {
+    "signer_group": "signet_0029",
+    "display_name": "Jason Ramirez",
+    "script": "latin",
+    "roles": [
+      "Board Witness Exec"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_024_5b_procedural",
+        "role": "Board Witness Exec",
+        "as": "companion",
+        "template": "proc_doc_11"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/029/07_029.png"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0078",
+    "display_name": "Jessica Campbell",
+    "script": "bengali",
+    "roles": [
+      "Second Authorised Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_023_5b_scanned",
+        "role": "Second Authorised Signatory",
+        "as": "companion",
+        "template": "bank_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/78/B-S-78-G-02.tif"
+    ]
+  },
+  {
+    "signer_group": "signet_0040",
+    "display_name": "Jessica Chen",
+    "script": "latin",
+    "roles": [
+      "Second Authorised Signatory",
+      "Bank Representative"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_007_1d_scanned",
+        "role": "Second Authorised Signatory",
+        "as": "additional",
+        "template": "bank_doc_09"
+      },
+      {
+        "case_id": "case_015_3b_scanned",
+        "role": "Bank Representative",
+        "as": "additional",
+        "template": "bank_doc_06"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/040/05_040.png",
+      "data/raw/signet/train/040/07_040.png"
+    ]
+  },
+  {
+    "signer_group": "signet_0009",
+    "display_name": "John Thompson",
+    "script": "latin",
+    "roles": [
+      "Bank Manager"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_001_1a_scanned",
+        "role": "Bank Manager",
+        "as": "primary",
+        "template": "bank_doc_08"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/009/009_24.PNG"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0005",
+    "display_name": "Kimberly Perez",
+    "script": "hindi",
+    "roles": [
+      "Account Holder",
+      "Applicant Agent"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_001_1a_scanned",
+        "role": "Account Holder",
+        "as": "additional",
+        "template": "bank_doc_08"
+      },
+      {
+        "case_id": "case_020_4b_procedural",
+        "role": "Applicant Agent",
+        "as": "primary",
+        "template": "proc_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/5/H-S-5-G-01.tif",
+      "data/raw/bhsig260_hindi/5/H-S-5-G-07.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0069",
+    "display_name": "Laura Young",
+    "script": "hindi",
+    "roles": [
+      "Company Secretary"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_006_1c_procedural",
+        "role": "Company Secretary",
+        "as": "companion",
+        "template": "proc_doc_07"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/69/H-S-69-G-08.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0110",
+    "display_name": "Liam Thomas",
+    "script": "hindi",
+    "roles": [
+      "Internal Compliance Controller"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_020_4b_procedural",
+        "role": "Internal Compliance Controller",
+        "as": "companion",
+        "template": "proc_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/110/H-S-110-G-02.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0011",
+    "display_name": "Linda Nguyen",
+    "script": "hindi",
+    "roles": [
+      "Borrower (Authorized Signatory)"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_015_3b_scanned",
+        "role": "Borrower (Authorized Signatory)",
+        "as": "primary",
+        "template": "bank_doc_06"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/11/H-S-011-F-05.tif"
+    ]
+  },
+  {
+    "signer_group": "signet_0048",
+    "display_name": "Margaret Mitchell",
+    "script": "latin",
+    "roles": [
+      "Borrower"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_013_3a_scanned",
+        "role": "Borrower",
+        "as": "primary",
+        "template": "bank_doc_05"
+      }
+    ],
+    "reference_images": [
+      "data/raw/signet/train/048/04_048.png"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0032",
+    "display_name": "Melissa Young",
+    "script": "bengali",
+    "roles": [
+      "Corporate Guarantor"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_002_1a_procedural",
+        "role": "Corporate Guarantor",
+        "as": "additional",
+        "template": "proc_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/32/B-S-32-G-03.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0072",
+    "display_name": "Nancy Ivanov",
+    "script": "bengali",
+    "roles": [
+      "Second Authorised Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_003_1b_scanned",
+        "role": "Second Authorised Signatory",
+        "as": "primary",
+        "template": "bank_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/72/B-S-72-G-07.tif"
+    ]
+  },
+  {
+    "signer_group": "signer_0038",
+    "display_name": "Noah Hill",
+    "script": "latin",
+    "roles": [
+      "Issuing Debtor"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_024_5b_procedural",
+        "role": "Issuing Debtor",
+        "as": "primary",
+        "template": "proc_doc_11"
+      }
+    ],
+    "reference_images": [
+      "data/raw/cedar/genuine/original_38_10.png"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0067",
+    "display_name": "Patricia Baker",
+    "script": "hindi",
+    "roles": [
+      "Borrower (Authorized Signatory)"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_005_1c_scanned",
+        "role": "Borrower (Authorized Signatory)",
+        "as": "primary",
+        "template": "bank_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/67/H-S-67-G-08.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0012",
+    "display_name": "Priya Tanaka",
+    "script": "hindi",
+    "roles": [
+      "Internal Compliance Controller"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_022_5a_procedural",
+        "role": "Internal Compliance Controller",
+        "as": "companion",
+        "template": "proc_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/12/H-S-12-G-04.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_bengali_0053",
+    "display_name": "Samantha Adams",
+    "script": "bengali",
+    "roles": [
+      "Authorized Chief Treasurer"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_014_3a_procedural",
+        "role": "Authorized Chief Treasurer",
+        "as": "primary",
+        "template": "proc_doc_12"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_bengali/53/B-S-53-G-01.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0017",
+    "display_name": "Sandra Harris",
+    "script": "hindi",
+    "roles": [
+      "Primary Debtor"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_002_1a_procedural",
+        "role": "Primary Debtor",
+        "as": "primary",
+        "template": "proc_doc_04"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/17/H-S-017-G-01.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0033",
+    "display_name": "Sandra Harris",
+    "script": "hindi",
+    "roles": [
+      "First Authorised Signatory",
+      "Second Authorised Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_017_4a_scanned",
+        "role": "First Authorised Signatory",
+        "as": "primary",
+        "template": "bank_doc_09"
+      },
+      {
+        "case_id": "case_017_4a_scanned",
+        "role": "Second Authorised Signatory",
+        "as": "companion",
+        "template": "bank_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/33/H-S-33-G-02.tif",
+      "data/raw/bhsig260_hindi/33/H-S-33-G-06.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0007",
+    "display_name": "Scott Taylor",
+    "script": "hindi",
+    "roles": [
+      "Authorized Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_013_3a_scanned",
+        "role": "Authorized Signatory",
+        "as": "additional",
+        "template": "bank_doc_05"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/7/H-S-7-G-06.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0044",
+    "display_name": "Scott Wright",
+    "script": "hindi",
+    "roles": [
+      "Beneficiary Sign-off"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_008_1d_procedural",
+        "role": "Beneficiary Sign-off",
+        "as": "primary",
+        "template": "proc_doc_15"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/44/H-S-44-G-07.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0121",
+    "display_name": "Stephanie Martinez",
+    "script": "hindi",
+    "roles": [
+      "Account Holder"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_019_4b_scanned",
+        "role": "Account Holder",
+        "as": "primary",
+        "template": "bank_doc_08"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/121/H-S-121-G-03.tif"
+    ]
+  },
+  {
+    "signer_group": "bhsig260_hindi_0036",
+    "display_name": "Stephen King",
+    "script": "hindi",
+    "roles": [
+      "First Authorised Signatory"
+    ],
+    "appears_in": [
+      {
+        "case_id": "case_023_5b_scanned",
+        "role": "First Authorised Signatory",
+        "as": "primary",
+        "template": "bank_doc_09"
+      }
+    ],
+    "reference_images": [
+      "data/raw/bhsig260_hindi/36/H-S-36-G-07.tif"
+    ]
+  }
+]
\ No newline at end of file
diff --git a/unified_pipeline/docs/unified_pipeline_Metrics_Thresholds_Formulas.pptx b/unified_pipeline/docs/unified_pipeline_Metrics_Thresholds_Formulas.pptx
new file mode 100644
index 0000000000000000000000000000000000000000..11c6ac3acb52fc69e1435150a0e8c979403fd61b
--- /dev/null
+++ b/unified_pipeline/docs/unified_pipeline_Metrics_Thresholds_Formulas.pptx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:605d2537b6d3128026d1451b9cf748754dc779f79a9fc21cdad7a6d917b66fe8
+size 3319145
diff --git a/unified_pipeline/main.py b/unified_pipeline/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..e410a25caade27c98cce886c1c4a7aed946d56cf
--- /dev/null
+++ b/unified_pipeline/main.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python
+"""CLI entry point for the synthetic signed bank document generation pipeline.
+
+Examples:
+    Generate 10 samples with all defaults::
+
+        python main.py
+
+    Generate 10,000 samples using a custom config::
+
+        python main.py --config configs/generate.yaml --count 10000
+
+    Override arbitrary nested config values from the command line::
+
+        python main.py --set placement.rotation_deg.max=6 --set augmentation.coffee_stain.enabled=true
+
+    Generate a single, fully reproducible sample from one template with a debug figure::
+
+        python main.py --count 1 --seed 42 --template bank_doc_03 --save-visualization
+
+    Request a specific difficulty score (not a tier) with a before/after preview::
+
+        python main.py --count 5 --target-difficulty 0.55 --save-before-after
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import sys
+from pathlib import Path
+
+from src.config.loader import load_config, save_resolved_config
+from src.generator import DatasetGenerator
+from src.utils.logging_utils import configure_logging, get_logger
+
+logger = get_logger(__name__)
+
+
+def build_arg_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Generate synthetic signed bank documents with rich ML-ready metadata.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--config", type=str, default=None, help="Path to a user YAML config, merged over configs/default.yaml.")
+    parser.add_argument("--count", type=int, default=None, help="Number of samples to generate (overrides config).")
+    parser.add_argument("--seed", type=int, default=None, help="Master random seed (overrides config).")
+    parser.add_argument("--workers", type=int, default=None, help="Number of worker processes (overrides config).")
+    parser.add_argument("--output", type=str, default=None, help="Output directory (overrides config).")
+    parser.add_argument("--template", type=str, default=None, help="Restrict generation to a single template id, e.g. bank_doc_03.")
+    parser.add_argument("--start-index", type=int, default=None, help="First sample index (overrides config).")
+    parser.add_argument("--overwrite", action="store_true", default=None, help="Regenerate samples even if their output files already exist.")
+    parser.add_argument("--save-visualization", action="store_true", default=None, help="Also save a debug visualization PNG per sample.")
+    parser.add_argument(
+        "--save-before-after", action="store_true", default=None,
+        help="Also save a before/after degradation comparison PNG per sample (clean render vs. final look).",
+    )
+    parser.add_argument(
+        "--target-difficulty", type=float, default=None, metavar="SCORE",
+        help="Request a specific difficulty score (e.g. 0.55), not a tier -- retries (see --difficulty-max-attempts) "
+             "until within --difficulty-tolerance, keeping the closest attempt if none matches.",
+    )
+    parser.add_argument("--difficulty-tolerance", type=float, default=None, metavar="DELTA", help="Acceptable |score - target| for --target-difficulty (default 0.05).")
+    parser.add_argument("--difficulty-max-attempts", type=int, default=None, metavar="N", help="Retry attempts for --target-difficulty (default 5).")
+    parser.add_argument(
+        "--set", dest="overrides", action="append", default=[],
+        metavar="key.path=value", help="Arbitrary dot-path config override; may be repeated.",
+    )
+    parser.add_argument("--log-level", type=str, default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"])
+    return parser
+
+
+def _collect_overrides(args: argparse.Namespace) -> list[str]:
+    overrides = list(args.overrides)
+    if args.count is not None:
+        overrides.append(f"generation.count={args.count}")
+    if args.workers is not None:
+        overrides.append(f"generation.workers={args.workers}")
+    if args.output is not None:
+        overrides.append(f"paths.output_dir={args.output}")
+    if args.start_index is not None:
+        overrides.append(f"generation.start_index={args.start_index}")
+    if args.overwrite:
+        overrides.append("generation.overwrite=true")
+    if args.save_visualization:
+        overrides.append("generation.save_visualization=true")
+    if args.save_before_after:
+        overrides.append("generation.save_before_after_preview=true")
+    if args.target_difficulty is not None:
+        overrides.append(f"generation.target_difficulty={args.target_difficulty}")
+    if args.difficulty_tolerance is not None:
+        overrides.append(f"generation.difficulty_tolerance={args.difficulty_tolerance}")
+    if args.difficulty_max_attempts is not None:
+        overrides.append(f"generation.difficulty_max_attempts={args.difficulty_max_attempts}")
+    if args.seed is not None:
+        overrides.append(f"seed={args.seed}")
+    if args.template is not None:
+        overrides.append(f"document.template={args.template}")
+    return overrides
+
+
+def main(argv: "list[str] | None" = None) -> int:
+    args = build_arg_parser().parse_args(argv)
+    configure_logging(level=getattr(logging, args.log_level))
+
+    overrides = _collect_overrides(args)
+    config = load_config(config_path=args.config, overrides=overrides)
+
+    logger.info("Starting generation: seed=%d, count=%d, workers=%d, output=%s",
+                config.seed, config.generation.count, config.generation.workers, config.paths.output_dir)
+
+    output_dir = Path(config.paths.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    save_resolved_config(config, output_dir / "resolved_config.yaml")
+
+    generator = DatasetGenerator(config)
+    summary = generator.generate()
+
+    summary_path = output_dir / "generation_summary.json"
+    summary_path.write_text(json.dumps(summary.as_dict(), indent=2), encoding="utf-8")
+    logger.info("Wrote generation summary to %s", summary_path)
+    logger.info(
+        "Done: %d/%d succeeded, %d failed, %d skipped (already existed). Difficulty tiers: %s",
+        summary.succeeded, summary.total, summary.failed, summary.skipped_existing, summary.tier_counts,
+    )
+    if summary.validation_failed:
+        logger.warning("%d sample(s) failed validation.", summary.validation_failed)
+
+    return 1 if summary.failed else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/unified_pipeline/pytest.ini b/unified_pipeline/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..6c91dc3607a67e829fbac511bbe04d00db52e5f7
--- /dev/null
+++ b/unified_pipeline/pytest.ini
@@ -0,0 +1,4 @@
+[pytest]
+pythonpath = .
+testpaths = tests
+python_files = test_*.py
diff --git a/unified_pipeline/requirements.txt b/unified_pipeline/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5058f3a903b3f9505f9a79a8c3e155ae7ae693ec
--- /dev/null
+++ b/unified_pipeline/requirements.txt
@@ -0,0 +1,16 @@
+numpy>=1.26
+opencv-python>=4.9
+scipy>=1.12
+Pillow>=10.2
+scikit-image>=0.22
+Shapely>=2.0
+PyYAML>=6.0
+pydantic>=2.6
+albumentations>=1.4
+omegaconf>=2.3
+matplotlib>=3.8
+tqdm>=4.66
+pytest>=8.0
+rich>=13.7
+questionary>=2.0
+streamlit>=1.35
diff --git a/unified_pipeline/scripts/build_degradation_slides.py b/unified_pipeline/scripts/build_degradation_slides.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb05808eadac4ac573d11660fe3727de2c5e2037
--- /dev/null
+++ b/unified_pipeline/scripts/build_degradation_slides.py
@@ -0,0 +1,306 @@
+#!/usr/bin/env python
+"""Append a before/after slide per augmentation effect, plus normal- and
+extreme-case multi-degradation slides, to Metrics_Thresholds_Formulas.pptx.
+
+Reuses the assets written by generate_before_after_assets.py and
+generate_case_assets.py (run those first). New slides replicate the deck's
+existing visual language exactly (navy header bar, orange accent line, teal
+section label, Calibri/Consolas body text, bottom-right page number) so they
+read as part of the same deck rather than a bolt-on.
+
+The new slides are inserted as a new section 8 ("Degradation gallery") and
+section 9 ("Multi-degradation cases"), placed right before the closing "Key
+takeaways" slide, and the Agenda slide (slide 2) is extended with the two new
+entries. A one-time backup of the original file is kept alongside it.
+
+Usage:
+    python scripts/build_degradation_slides.py
+"""
+
+from __future__ import annotations
+
+import copy
+import json
+import shutil
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from pptx import Presentation
+from pptx.dml.color import RGBColor
+from pptx.enum.shapes import MSO_SHAPE
+from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
+from pptx.util import Emu, Pt
+from PIL import Image
+
+from scripts.generate_before_after_assets import (
+    ALL_EFFECTS, DISPLAY_NAME, ZOOM_EFFECTS,
+)
+from scripts.generate_multiple_degradation_extreme_gallery import _fmt_effect
+
+ROOT = Path(__file__).resolve().parents[1]
+PPTX_PATH = ROOT / "Metrics_Thresholds_Formulas.pptx"
+BACKUP_PATH = ROOT / "Metrics_Thresholds_Formulas.backup.pptx"
+DEG_ROOT = ROOT / "degradation"
+
+# ---------------------------------------------------------------- palette --
+NAVY, TEAL, ORANGE = "0F2A43", "1B7A7A", "E07A2C"
+LIGHT_TEAL, GRAY, LIGHT_BG, TEXT_DARK, WHITE = "9BC5C5", "6B767C", "F4F7F8", "222A30", "FFFFFF"
+
+
+def rgb(hexstr: str) -> RGBColor:
+    return RGBColor.from_string(hexstr)
+
+
+# ------------------------------------------------------------- geometry ---
+SLIDE_W, SLIDE_H = Emu(12191695), Emu(6858000)
+HEADER_H, ACCENT_H = Emu(1051560), Emu(38100)
+TITLE_X = Emu(502920)
+CONTENT_X, CONTENT_W = Emu(640080), Emu(10881360)
+FOOTER_X, FOOTER_Y = Emu(11475720), Emu(6510528)
+FOOTER_W, FOOTER_H = Emu(640080), Emu(274320)
+
+LEFT_X, RIGHT_X = Emu(640080), Emu(6263640)
+COL_W = Emu(5257800)
+LABEL_Y, LABEL_H = Emu(1554480), Emu(365760)
+IMG_Y = Emu(LABEL_Y + LABEL_H + Emu(91440))
+
+CAPTION_Y = Emu(1150000)
+CAPTION_H = Emu(320040)
+
+# ------------------------------------------------------ formula captions --
+FORMULAS = {
+    "rotation": "extra_deg = U(-1,1) × strength × 8.0",
+    "stroke_dilation": "ksize = round(1 + strength×5)  [odd]",
+    "ink_fading": "mult = clip(1 − strength×(1−field)×0.9, .05, 1)",
+    "ink_bleed": "ksize = round(1+strength×4); halo floor = .35+.35×strength",
+    "motion_blur": "length = round(3 + strength×16)",
+    "gaussian_blur": "sigma = 0.2 + strength×2.2",
+    "perspective": "scale = 0.01 + strength×0.09",
+    "contrast": "contrast_limit = ±(strength×0.45)",
+    "gamma": "delta = round(strength×45); gamma_limit = (100−delta, 100+delta), lo≥40",
+    "coffee_stain": "n_blobs = 1+randint(0,1+round(strength×3)); opacity = .10+.30×strength",
+    "stain": "radius = (.06+.16×strength)×min(w,h); opacity scales with strength",
+    "shadow": "darkness = 0.15+0.55×strength; band/radius/fold-width scale with strength",
+    "crumple": "amplitude = strength×min(w,h)×0.015",
+    "discoloration": "tint strength scales with strength per tone",
+    "page_tilt": "angle = U(-1,1) × (1.5 + strength×6.5)",
+    "dust": "n_specks = (h×w)×0.00003×(1+strength×6)",
+    "scanner_streaks": "n_streaks = 1+int(strength×6)",
+    "scanner_noise": "GaussNoise std = 0.01 + strength×0.12",
+    "salt_pepper": "amount = 0.002 + strength×0.09",
+    "jpeg_compression": "quality = randint(min_quality, max_quality) — NOT driven by strength",
+}
+
+
+def short_list(names: list[str], limit: int = 8) -> str:
+    if len(names) <= limit:
+        return ", ".join(names) if names else "none"
+    return ", ".join(names[:limit]) + f", +{len(names) - limit} more"
+
+
+# ----------------------------------------------------------- primitives ---
+def add_rect(slide, x, y, w, h, color: str):
+    shp = slide.shapes.add_shape(MSO_SHAPE.RECTANGLE, x, y, w, h)
+    shp.fill.solid()
+    shp.fill.fore_color.rgb = rgb(color)
+    shp.line.fill.background()
+    return shp
+
+
+def add_text(
+    slide, x, y, w, h, text, size, color, *, bold=False, font="Calibri",
+    align=PP_ALIGN.LEFT, anchor=None, word_wrap=True,
+):
+    tb = slide.shapes.add_textbox(x, y, w, h)
+    tf = tb.text_frame
+    tf.margin_left = tf.margin_right = tf.margin_top = tf.margin_bottom = 0
+    tf.word_wrap = word_wrap
+    if anchor is not None:
+        tf.vertical_anchor = anchor
+    lines = text.split("\n")
+    for i, line in enumerate(lines):
+        p = tf.paragraphs[0] if i == 0 else tf.add_paragraph()
+        p.alignment = align
+        run = p.add_run()
+        run.text = line
+        run.font.size = Pt(size)
+        run.font.bold = bold
+        run.font.name = font
+        run.font.color.rgb = rgb(color)
+    return tb
+
+
+def add_bg(slide):
+    add_rect(slide, Emu(0), Emu(0), SLIDE_W, SLIDE_H, WHITE)
+
+
+def add_header(slide, section_label: str, title: str, page_num: int):
+    add_rect(slide, Emu(0), Emu(0), SLIDE_W, HEADER_H, NAVY)
+    add_rect(slide, Emu(0), HEADER_H, SLIDE_W, ACCENT_H, ORANGE)
+    add_text(slide, TITLE_X, Emu(128016), Emu(9144000), Emu(274320), section_label, 12, LIGHT_TEAL, bold=True)
+    add_text(slide, TITLE_X, Emu(384048), Emu(10515600), Emu(594360), title, 28, WHITE, bold=True)
+    add_text(
+        slide, FOOTER_X, FOOTER_Y, FOOTER_W, FOOTER_H, str(page_num), 11, GRAY,
+        align=PP_ALIGN.RIGHT,
+    )
+
+
+def fit_picture(slide, img_path: Path, box_x, box_y, box_w, box_h):
+    iw, ih = Image.open(img_path).size
+    scale = min(box_w / iw, box_h / ih)
+    disp_w, disp_h = int(iw * scale), int(ih * scale)
+    left = int(box_x + (box_w - disp_w) / 2)
+    top = int(box_y + (box_h - disp_h) / 2)
+    slide.shapes.add_picture(str(img_path), left, top, width=disp_w, height=disp_h)
+
+
+def add_panel_label(slide, x, color, text):
+    add_rect(slide, x, LABEL_Y, COL_W, LABEL_H, color)
+    add_text(
+        slide, x, LABEL_Y, COL_W, LABEL_H, text, 14, WHITE, bold=True,
+        align=PP_ALIGN.CENTER, anchor=MSO_ANCHOR.MIDDLE, word_wrap=False,
+    )
+
+
+# -------------------------------------------------------------- slides ----
+def build_before_after_slide(prs, layout, effect: str, page_num: int, manifest: dict):
+    slide = prs.slides.add_slide(layout)
+    add_bg(slide)
+    add_header(slide, "8. DEGRADATION GALLERY", DISPLAY_NAME[effect], page_num)
+
+    rec = manifest[effect]
+    resolved = _fmt_effect(effect, {"strength": rec["strength"], "params": rec["params"] or {}})
+    caption = (
+        f"{FORMULAS[effect]}     |     resolved: {resolved}     |     "
+        f"difficulty after = {rec['difficulty_score']:.3f} ({rec['difficulty_tier']})"
+    )
+    add_text(slide, CONTENT_X, CAPTION_Y, CONTENT_W, CAPTION_H, caption, 12, GRAY, font="Consolas")
+
+    add_panel_label(slide, LEFT_X, TEAL, "BEFORE  -  clean render")
+    add_panel_label(slide, RIGHT_X, ORANGE, f"AFTER  -  {DISPLAY_NAME[effect]} (strength={rec['strength']:.2f})")
+
+    img_bottom = Emu(6100000)
+    img_h = Emu(img_bottom - IMG_Y)
+    before_name = "before_zoom.png" if effect in ZOOM_EFFECTS else "before.png"
+    after_name = "after_zoom.png" if effect in ZOOM_EFFECTS else "after.png"
+    fit_picture(slide, DEG_ROOT / effect / before_name, LEFT_X, IMG_Y, COL_W, img_h)
+    fit_picture(slide, DEG_ROOT / effect / after_name, RIGHT_X, IMG_Y, COL_W, img_h)
+    return slide
+
+
+def build_case_slide(prs, layout, kind: str, page_num: int):
+    slide = prs.slides.add_slide(layout)
+    add_bg(slide)
+    title = "Normal-severity combinations" if kind == "normal" else "Extreme-severity combinations"
+    add_header(slide, "9. MULTI-DEGRADATION CASES", title, page_num)
+
+    if kind == "normal":
+        folder = ROOT / "multiple_degradation" / "case_assets"
+        subtitle = (
+            "configs/generate.yaml -- every effect independently rolls its own realistic "
+            "probability per sample (mixed, moderate combinations)"
+        )
+        color = TEAL
+    else:
+        folder = ROOT / "multiple_degradation_extreme" / "case_assets"
+        subtitle = (
+            "worst case -- every effect probability=0.85, strength 0.7-1.0, plus heavy "
+            "misplacement / faint+blurred ink (see scripts/generate_multiple_degradation_extreme_gallery.py)"
+        )
+        color = ORANGE
+
+    manifest = json.loads((folder / "case_manifest.json").read_text())
+    add_text(slide, CONTENT_X, CAPTION_Y, CONTENT_W, CAPTION_H, subtitle, 12, GRAY, font="Consolas")
+
+    img_bottom = Emu(5850000)
+    img_h = Emu(img_bottom - IMG_Y)
+    cap_y = Emu(img_bottom + Emu(45720))
+    cap_h = Emu(600000)
+
+    for i, x in enumerate((LEFT_X, RIGHT_X)):
+        key = f"case_{i + 1}"
+        rec = manifest[key]
+        add_panel_label(slide, x, color, f"Sample {i + 1}  -  {rec['n_applied']}/{rec['n_total']} effects applied")
+        fit_picture(slide, folder / f"{key}.png", x, IMG_Y, COL_W, img_h)
+        cap = (
+            f"difficulty = {rec['difficulty_score']:.3f} ({rec['difficulty_tier']})   |   "
+            f"placement: rot={rec['placement']['rotation_deg']:.1f} deg  "
+            f"scale={rec['placement']['scale_factor']:.2f}  misplaced={rec['placement']['misplaced']}\n"
+            f"applied: {short_list(rec['applied_effects'])}"
+        )
+        add_text(slide, x, cap_y, COL_W, cap_h, cap, 10, TEXT_DARK, font="Consolas")
+    return slide
+
+
+# ---------------------------------------------------------- agenda edit ---
+def extend_agenda(prs):
+    slide = prs.slides[1]
+    target = None
+    for shape in slide.shapes:
+        if shape.name == "TextBox 7":
+            target = shape
+            break
+    if target is None:
+        print("WARNING: could not find agenda TextBox 7 -- skipping agenda update")
+        return
+
+    tf = target.text_frame
+    n_original = len(tf.paragraphs)
+    last_p = tf.paragraphs[-1]._p
+    new_texts = (
+        "8.  Degradation gallery — before & after, all 20 effects",
+        "9.  Multi-degradation cases — normal vs. extreme severity",
+    )
+    for text in new_texts:
+        new_p = copy.deepcopy(last_p)
+        run_elm = new_p.findall(".//{http://schemas.openxmlformats.org/drawingml/2006/main}t")
+        run_elm[0].text = text
+        last_p.addnext(new_p)
+        last_p = new_p
+
+    # Shape height doesn't clip a top-anchored textbox -- text simply overflows past
+    # it -- so what actually matters is total rendered text height staying inside the
+    # slide. 9 lines at the original 22pt ran ~24pt past the bottom edge, so shrink
+    # every run down to 20pt (measured to comfortably clear the slide bottom).
+    for para in tf.paragraphs:
+        for run in para.runs:
+            run.font.size = Pt(20)
+
+
+# ---------------------------------------------------------------- main ----
+def main():
+    if not BACKUP_PATH.exists():
+        shutil.copy2(PPTX_PATH, BACKUP_PATH)
+        print(f"Backed up original deck -> {BACKUP_PATH}")
+
+    manifest = json.loads((DEG_ROOT / "before_after_manifest.json").read_text())
+
+    prs = Presentation(str(PPTX_PATH))
+    layout = prs.slides[2].slide_layout  # "Blank" layout used by all content slides
+
+    sld_id_lst = prs.slides._sldIdLst
+    closing_sld_id = sld_id_lst[-1]  # "Key takeaways" slide, must stay last
+
+    extend_agenda(prs)
+
+    page_num = 19
+    for effect in ALL_EFFECTS:
+        build_before_after_slide(prs, layout, effect, page_num, manifest)
+        page_num += 1
+
+    build_case_slide(prs, layout, "normal", page_num)
+    page_num += 1
+    build_case_slide(prs, layout, "extreme", page_num)
+    page_num += 1
+
+    sld_id_lst.remove(closing_sld_id)
+    sld_id_lst.append(closing_sld_id)
+
+    prs.save(str(PPTX_PATH))
+    print(f"Saved {PPTX_PATH} -- {len(prs.slides.__iter__.__self__._sldIdLst)} slides total")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/degrade_cli.py b/unified_pipeline/scripts/degrade_cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..5acf63135c4c228ae739837c1dfc934f95f6c3c7
--- /dev/null
+++ b/unified_pipeline/scripts/degrade_cli.py
@@ -0,0 +1,279 @@
+#!/usr/bin/env python
+"""Interactive terminal wizard for generating synthetic signed documents with
+hand-picked degradation effects.
+
+A friendlier front end over main.py's --set override system: arrow-key menus
+and checkboxes instead of memorizing config dot-paths. Same pipeline underneath
+(GeneratorContext), so output is identical to what main.py would produce with
+the equivalent --set flags.
+
+Usage:
+    python scripts/degrade_cli.py
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import questionary
+from questionary import Choice, Separator
+from rich.console import Console
+from rich.panel import Panel
+from rich.progress import BarColumn, Progress, SpinnerColumn, TextColumn, TimeElapsedColumn
+from rich.table import Table
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.templates.template_loader import TemplateLoader
+from src.utils.image_io import save_image_rgb
+from src.visualization.visualizer import save_visualization
+from scripts.generate_before_after_assets import ALL_EFFECTS, DISPLAY_NAME, DOCUMENT_EFFECTS, SIGNATURE_EFFECTS
+from scripts.generate_multiple_degradation_extreme_gallery import build_overrides as extreme_overrides
+
+ROOT = Path(__file__).resolve().parents[1]
+console = Console()
+
+# ---- palette, matched to Metrics_Thresholds_Formulas.pptx -----------------
+NAVY, TEAL, ORANGE, GRAY = "#0F2A43", "#1B7A7A", "#E07A2C", "#6B767C"
+
+QSTYLE = questionary.Style([
+    ("qmark", f"fg:{ORANGE} bold"),
+    ("question", "bold"),
+    ("answer", f"fg:{TEAL} bold"),
+    ("pointer", f"fg:{ORANGE} bold"),
+    ("highlighted", f"fg:{TEAL} bold"),
+    ("selected", f"fg:{TEAL}"),
+    ("instruction", f"fg:{GRAY}"),
+    ("separator", f"fg:{GRAY}"),
+])
+
+
+def banner() -> None:
+    console.print(Panel.fit(
+        "[bold white]Synthetic Signed Document Generator[/bold white]\n"
+        f"[{TEAL}]pick degradations  ->  generate  ->  inspect[/{TEAL}]",
+        border_style=TEAL, padding=(1, 4),
+    ))
+
+
+def discover_templates() -> list[tuple[str, str]]:
+    cfg = load_config()
+    loader = TemplateLoader(cfg.paths.image_dir, cfg.paths.annotations_dir)
+    loader.discover()
+    return sorted((tid, t.annotation.document_type) for tid, t in loader._templates.items())
+
+
+def valid_float01(v: str) -> "bool | str":
+    try:
+        f = float(v)
+    except ValueError:
+        return "Enter a number between 0.0 and 1.0"
+    return True if 0.0 <= f <= 1.0 else "Must be between 0.0 and 1.0"
+
+
+def valid_posint(v: str) -> "bool | str":
+    return True if v.isdigit() and int(v) > 0 else "Enter a positive whole number"
+
+
+# ------------------------------------------------------------- questions ---
+def ask_mode() -> str:
+    return questionary.select(
+        "How should degradations be applied?",
+        choices=[
+            Choice(title="Pick effects - isolated  (ONLY the chosen effects fire, nothing else)", value="isolate"),
+            Choice(title="Pick effects - mixed     (chosen effects forced on, others may also roll)", value="mixed"),
+            Choice(title="Preset - normal severity  (realistic random mix, configs/generate.yaml)", value="normal"),
+            Choice(title="Preset - extreme severity (worst case, everything cranked)", value="extreme"),
+        ],
+        style=QSTYLE,
+    ).ask()
+
+
+def ask_template() -> "str | None":
+    templates = discover_templates()
+    choices = [Choice(title="Random each sample", value=None)]
+    choices += [Choice(title=f"{tid}  ({doc_type})", value=tid) for tid, doc_type in templates]
+    return questionary.select("Which document template?", choices=choices, style=QSTYLE).ask()
+
+
+def ask_effects() -> list[str]:
+    choices = [Separator("-- signature-level --")]
+    choices += [Choice(title=DISPLAY_NAME[e], value=e) for e in SIGNATURE_EFFECTS]
+    choices += [Separator("-- document-level --")]
+    choices += [Choice(title=DISPLAY_NAME[e], value=e) for e in DOCUMENT_EFFECTS]
+    selected = questionary.checkbox(
+        "Pick degradations (space to toggle, enter to confirm)", choices=choices, style=QSTYLE,
+    ).ask()
+    if selected is None:
+        raise KeyboardInterrupt
+    if not selected:
+        console.print(f"[{ORANGE}]Pick at least one effect.[/{ORANGE}]")
+        return ask_effects()
+    return selected
+
+
+def ask_strengths(effects: list[str]) -> dict[str, float]:
+    global_raw = questionary.text(
+        "Strength for all selected effects (0.0-1.0)", default="0.75",
+        validate=valid_float01, style=QSTYLE,
+    ).ask()
+    global_strength = float(global_raw)
+    strengths = {e: global_strength for e in effects}
+    if questionary.confirm("Customize strength per effect?", default=False, style=QSTYLE).ask():
+        for e in effects:
+            raw = questionary.text(
+                f"  {DISPLAY_NAME[e]} strength", default=str(global_strength),
+                validate=valid_float01, style=QSTYLE,
+            ).ask()
+            strengths[e] = float(raw)
+    return strengths
+
+
+def ask_count() -> int:
+    raw = questionary.text("How many samples?", default="3", validate=valid_posint, style=QSTYLE).ask()
+    return int(raw)
+
+
+def ask_save_viz() -> bool:
+    return questionary.confirm(
+        "Also save the debug-panel visualization (crop / mask / metrics / difficulty)?",
+        default=True, style=QSTYLE,
+    ).ask()
+
+
+def ask_output_dir() -> Path:
+    default = f"output/interactive_{datetime.now():%Y%m%d_%H%M%S}"
+    raw = questionary.text("Output folder", default=default, style=QSTYLE).ask()
+    return ROOT / raw
+
+
+# ------------------------------------------------------------ overrides ----
+def build_pick_overrides(mode: str, template: "str | None", effects: list[str], strengths: dict[str, float]) -> list[str]:
+    overrides = ["generation.run_validation=false"]
+    if template:
+        overrides.append(f"document.template={template}")
+
+    if mode == "isolate":
+        for name in ALL_EFFECTS:
+            on = name in effects
+            overrides.append(f"augmentation.{name}.enabled={'true' if on else 'false'}")
+            overrides.append(f"augmentation.{name}.probability={'1.0' if on else '0.0'}")
+        for name in effects:
+            overrides.append(f"augmentation.{name}.strength={strengths[name]}")
+    else:  # mixed
+        for name in effects:
+            overrides.append(f"augmentation.{name}.enabled=true")
+            overrides.append(f"augmentation.{name}.probability=1.0")
+            overrides.append(f"augmentation.{name}.strength={strengths[name]}")
+    return overrides
+
+
+# ---------------------------------------------------------------- report ---
+def show_plan(mode: str, template, effects, strengths, count: int, out_dir: Path) -> None:
+    table = Table(title="Plan", title_style=f"bold {NAVY}", show_header=False, border_style=GRAY)
+    table.add_row("Mode", mode)
+    table.add_row("Template", template or "random per sample")
+    if effects:
+        table.add_row("Effects", ", ".join(f"{DISPLAY_NAME[e]} ({strengths[e]:.2f})" for e in effects))
+    table.add_row("Samples", str(count))
+    table.add_row("Output", str(out_dir))
+    console.print(table)
+
+
+def show_results(results: list[tuple[str, float, str, list[str]]], out_dir: Path) -> None:
+    table = Table(title="Results", border_style=GRAY, header_style=f"bold white on {NAVY}")
+    table.add_column("Sample")
+    table.add_column("Difficulty")
+    table.add_column("Tier")
+    table.add_column("Effects applied")
+    tier_color = {"easy": "green", "medium": ORANGE, "hard": "red"}
+    for sid, score, tier, applied in results:
+        table.add_row(
+            sid, f"{score:.3f}", f"[{tier_color.get(tier, 'white')}]{tier}[/]",
+            ", ".join(applied) if applied else "[dim]none[/dim]",
+        )
+    console.print(table)
+    console.print(f"\n[{TEAL}]Wrote {len(results)} sample(s) -> {out_dir}[/{TEAL}]")
+
+
+# ------------------------------------------------------------------ main ---
+def main() -> int:
+    banner()
+    try:
+        mode = ask_mode()
+        if mode is None:
+            raise KeyboardInterrupt
+
+        template, effects, strengths = None, [], {}
+        if mode in ("isolate", "mixed"):
+            template = ask_template()
+            effects = ask_effects()
+            strengths = ask_strengths(effects)
+
+        count = ask_count()
+        save_viz = ask_save_viz()
+        out_dir = ask_output_dir()
+
+        show_plan(mode, template, effects, strengths, count, out_dir)
+        if not questionary.confirm("Generate now?", default=True, style=QSTYLE).ask():
+            console.print("Cancelled.")
+            return 0
+
+        if mode == "normal":
+            config = load_config(
+                config_path=ROOT / "configs" / "generate.yaml",
+                overrides=["document.template=bank_doc_05", "generation.run_validation=false"],
+            )
+        elif mode == "extreme":
+            config = load_config(overrides=extreme_overrides())
+        else:
+            config = load_config(overrides=build_pick_overrides(mode, template, effects, strengths))
+
+        out_dir.mkdir(parents=True, exist_ok=True)
+        ctx = GeneratorContext(config)
+        prefix, digits = config.generation.filename_prefix, config.generation.filename_digits
+
+        results = []
+        with Progress(
+            SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
+            BarColumn(complete_style=TEAL), TextColumn("{task.completed}/{task.total}"),
+            TimeElapsedColumn(), console=console,
+        ) as progress:
+            task = progress.add_task("Generating", total=count)
+            for i in range(1, count + 1):
+                metadata, final_image, _ = ctx.run_sample(i)
+                sample_id = f"{prefix}_{i:0{digits}d}"
+
+                save_image_rgb(out_dir / f"{sample_id}.png", final_image)
+                ctx.metadata_exporter.export(metadata, out_dir / f"{sample_id}.json")
+                if save_viz:
+                    save_visualization(out_dir / f"{sample_id}_viz.png", final_image, metadata)
+
+                applied = [n for n, r in metadata["augmentation"]["effects"].items() if r["applied"]]
+                results.append((sample_id, metadata["difficulty"]["score"], metadata["difficulty"]["tier"], applied))
+                progress.advance(task)
+
+        console.print()
+        show_results(results, out_dir)
+
+        if questionary.confirm("Open the output folder now?", default=True, style=QSTYLE).ask():
+            if os.name == "nt":
+                os.startfile(out_dir)  # noqa: S606
+            else:
+                console.print(str(out_dir))
+
+        return 0
+
+    except KeyboardInterrupt:
+        console.print("\n[dim]Cancelled.[/dim]")
+        return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/unified_pipeline/scripts/detect_cosmetic_field_coordinates.py b/unified_pipeline/scripts/detect_cosmetic_field_coordinates.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa4bd1a1f5bcf3ed900055ae1b23ec2365dbb604
--- /dev/null
+++ b/unified_pipeline/scripts/detect_cosmetic_field_coordinates.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+"""One-time measurement pass: detect each scanned template's cosmetic-field
+anchor line and cache it into ``annotations/<template_id>.json``, alongside
+the existing hand-measured ``signature_boxes``.
+
+HOW EACH COORDINATE IS ACTUALLY MEASURED (in order):
+
+1. A generous SEARCH REGION is hand-picked per field, by visually
+   inspecting the rendered template image once (a rectangle expressed as
+   ``(x0, y0, x1, y1)`` fractions of the page's width/height -- see
+   ``FIELD_REGION_RECIPES`` below). This step is the only "by eye" part,
+   and it only needs to be generous/approximately right, not precise --
+   the next step finds the exact line within it.
+2. Within that region, the SAME classical-CV horizontal-rule-line detector
+   already used to auto-detect signature boxes when no manual annotation
+   exists (``src/templates/box_detector.py`` -- binarize, morphological
+   opening with a wide horizontal kernel to isolate long thin dark runs,
+   merge nearby fragments) finds the actual blank underline or table
+   row-divider PIXEL-PERFECTLY. This is deterministic given the same
+   image, so it only needs to run once per template, ever.
+3. The result -- (field name, "above_line" or "below_line" mode, and the
+   detected line's exact x/y/width) -- is written into
+   ``annotations/<template_id>.json``'s ``pages[0].cosmetic_fields``,
+   the same file (and same coordinate space) ``signature_boxes`` already
+   lives in.
+
+This script is run manually, NOT as part of normal generation -- once a
+template's cosmetic_fields are cached in its annotation file,
+:func:`src.cosmetics.scanned_cosmetic_fields.fill_scanned_cosmetic_fields`
+just reads them directly, no image analysis at generation time.
+
+Usage:
+    python scripts/detect_cosmetic_field_coordinates.py [--force]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+import numpy as np
+from PIL import Image
+
+from src.templates.box_detector import _binarize, _detect_horizontal_lines, _merge_nearby_lines
+from src.templates.template_loader import TemplateLoader
+from src.utils.image_io import load_image_rgb
+
+ROOT = Path(__file__).resolve().parents[1]
+
+# Search regions -- the ONLY hand-eyeballed input. See the module docstring.
+# Kept here (not imported from scanned_cosmetic_fields.py) so this script is
+# the single place that owns "how to (re)measure a template from scratch";
+# scanned_cosmetic_fields.py only ever reads the already-measured result.
+FIELD_REGION_RECIPES: "dict[str, list[dict]]" = {
+    # bank_doc_03/04/05/06/08 all have TWO signature boxes, and (confirmed
+    # by visually cropping each template around every candidate line) each
+    # box has its OWN "Print Name:"-style line -- not one shared field.
+    # ``box_id`` ties a field to the signature box whose signer's name it
+    # should get filled with, so ``fill_scanned_cosmetic_fields()`` never
+    # has to guess which of two blank lines belongs to which party.
+    #
+    # bank_doc_03's sig1 region originally also contained a WIDER "By:"
+    # rule line above the actual "Print Name:" line -- the old single-field
+    # recipe's generous region let ``_find_line_in_region``'s "pick the
+    # widest line" heuristic grab "By:" instead, a real (if invisible)
+    # placement bug fixed here by narrowing the region to the exact
+    # "Print Name:" line's y-band instead of the whole By/Print Name/Title
+    # block.
+    "bank_doc_03": [
+        {"field": "name_sig1", "mode": "above_line", "region_frac": (0.547, 0.814, 0.742, 0.850), "box_id": "sig1"},
+        {"field": "name_sig2", "mode": "above_line", "region_frac": (0.771, 0.814, 0.967, 0.850), "box_id": "sig2"},
+    ],
+    "bank_doc_04": [
+        {"field": "name_sig1", "mode": "above_line", "region_frac": (0.547, 0.855, 0.742, 0.873), "box_id": "sig1"},
+        {"field": "name_sig2", "mode": "above_line", "region_frac": (0.752, 0.844, 0.977, 0.884), "box_id": "sig2"},
+    ],
+    "bank_doc_05": [
+        {"field": "name_sig1", "mode": "above_line", "region_frac": (0.03, 0.75, 0.48, 0.88), "box_id": "sig1"},
+        {"field": "name_sig2", "mode": "above_line", "region_frac": (0.502, 0.835, 0.946, 0.864), "box_id": "sig2"},
+    ],
+    "bank_doc_06": [
+        {"field": "name_sig1", "mode": "above_line", "region_frac": (0.098, 0.823, 0.478, 0.841), "box_id": "sig1"},
+        {"field": "name_sig2", "mode": "above_line", "region_frac": (0.576, 0.814, 0.889, 0.855), "box_id": "sig2"},
+    ],
+    "bank_doc_07": [{"field": "name", "mode": "above_line", "region_frac": (0.28, 0.28, 0.72, 0.35)}],
+    "bank_doc_08": [
+        # "Name of Account Holder:" (sig1) and "Bank Manager's Name:" (sig2)
+        # -- the latter was never measured before, so it was always left
+        # blank no matter what filled the Account Holder's line.
+        {"field": "name_sig1", "mode": "above_line", "region_frac": (0.14, 0.225, 0.48, 0.27), "box_id": "sig1"},
+        {"field": "name_sig2", "mode": "above_line", "region_frac": (0.684, 0.296, 0.986, 0.311), "box_id": "sig2"},
+    ],
+    "bank_doc_09": [{"field": "company_name", "mode": "below_line", "region_frac": (0.05, 0.153, 0.55, 0.168)}],
+}
+
+
+def _find_line_in_region(gray: "np.ndarray", region_px: "tuple[int, int, int, int]") -> "tuple[int, int, int] | None":
+    x0, y0, x1, y1 = region_px
+    crop = gray[y0:y1, x0:x1]
+    if crop.size == 0:
+        return None
+    binary = _binarize(crop)
+    lines = _merge_nearby_lines(_detect_horizontal_lines(binary, min_width_frac=0.15))
+    if not lines:
+        return None
+    best = max(lines, key=lambda l: l.w)
+    return (x0 + best.x, y0 + best.y, best.w)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("--force", action="store_true", help="Re-measure and overwrite even if cosmetic_fields already exist.")
+    args = parser.parse_args()
+
+    loader = TemplateLoader(ROOT / "templates" / "scanned", ROOT / "annotations")
+    templates = loader.discover()
+
+    for template_id, recipes in FIELD_REGION_RECIPES.items():
+        template = templates[template_id]
+        annotation_path = ROOT / "annotations" / f"{template_id}.json"
+        data = json.loads(annotation_path.read_text(encoding="utf-8"))
+        page = data["pages"][0]
+
+        if page.get("cosmetic_fields") and not args.force:
+            print(f"{template_id}: already has cosmetic_fields, skipping (use --force to re-measure)")
+            continue
+
+        img = load_image_rgb(template.image_path)
+        gray = np.array(Image.fromarray(img).convert("L"))
+        h, w = gray.shape[:2]
+
+        measured = []
+        for spec in recipes:
+            fx0, fy0, fx1, fy1 = spec["region_frac"]
+            region_px = (int(fx0 * w), int(fy0 * h), int(fx1 * w), int(fy1 * h))
+            found = _find_line_in_region(gray, region_px)
+            if found is None:
+                print(f"{template_id}: field '{spec['field']}' -- NO LINE FOUND in region {spec['region_frac']}, skipped")
+                continue
+            line_x, line_y, line_w = found
+            entry = {"field": spec["field"], "mode": spec["mode"], "line_x": line_x, "line_y": line_y, "line_w": line_w}
+            if spec.get("box_id"):
+                entry["box_id"] = spec["box_id"]
+            measured.append(entry)
+            print(f"{template_id}: field '{spec['field']}' -> line=({line_x},{line_y},w={line_w}) box_id={spec.get('box_id')}")
+
+        page["cosmetic_fields"] = measured
+        annotation_path.write_text(json.dumps(data, indent=2), encoding="utf-8")
+
+    print("\nDone. Re-run with --force to re-measure from scratch (e.g. after a template image changes).")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_before_after_assets.py b/unified_pipeline/scripts/generate_before_after_assets.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc5c3baae1786742b72d73ff9d7c90c922d46e40
--- /dev/null
+++ b/unified_pipeline/scripts/generate_before_after_assets.py
@@ -0,0 +1,177 @@
+#!/usr/bin/env python
+"""Generate paired before/after images for every augmentation effect, for slides.
+
+For each of the 25 configurable effects, renders two samples that are
+identical in every respect (same template, signature, placement, box) except
+that one has every augmentation disabled ("before") and the other has just
+that one effect forced on at a fixed strength ("after"). Because placement is
+frozen and both runs share the same (seed, sample_index), the underlying
+document/signature/box are pixel-identical up to the effect itself.
+
+Signature-level effects are saved as a tight crop around the signature box
+(where the effect is visible at slide scale); document-level effects are
+saved as the full page. Also writes a JSON manifest with the resolved
+strength/params/formula description used per effect, for slide captions.
+
+Usage:
+    python scripts/generate_before_after_assets.py
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from PIL import Image
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.visualization.visualizer import _crop_region
+
+SIGNATURE_EFFECTS = [
+    "rotation", "stroke_dilation",
+    "ink_fading", "ink_bleed", "motion_blur", "gaussian_blur",
+]
+DOCUMENT_EFFECTS = [
+    "perspective", "contrast", "gamma", "coffee_stain", "stain",
+    "shadow", "crumple", "discoloration", "page_tilt", "dust", "scanner_streaks",
+    "scanner_noise", "salt_pepper", "jpeg_compression",
+]
+ALL_EFFECTS = SIGNATURE_EFFECTS + DOCUMENT_EFFECTS
+
+OUT_ROOT = Path(__file__).resolve().parents[1] / "degradation"
+TEMPLATE_ID = "bank_doc_05"
+FORCED_STRENGTH = 1.0  # max strength: matches the "range at strength=1.0" figures already on the formula slides
+SAMPLE_INDEX = 2  # picked for a bolder/higher-ink-fill signature than index 1, more visible at slide scale
+CROP_PAD = 45
+CROP_UPSCALE = 2
+
+# Purely tonal/textural document effects are nearly invisible at full-page slide
+# scale, so also cut a fixed, upscaled body-text zoom for these (kept identical
+# between before/after so the comparison is apples-to-apples). Structural effects
+# (crumple, page_tilt, perspective, shadow, scanner_streaks) already read clearly
+# on the full page and don't need one.
+ZOOM_EFFECTS = {
+    "contrast", "gamma", "coffee_stain", "stain",
+    "discoloration", "dust", "scanner_noise", "salt_pepper", "jpeg_compression",
+}
+ZOOM_BOX = (40, 110, 460, 340)  # (x1, y1, x2, y2) in full-page pixel space
+
+DISPLAY_NAME = {
+    "rotation": "Rotation jitter",
+    "stroke_dilation": "Stroke dilation",
+    "ink_fading": "Ink fading",
+    "ink_bleed": "Ink bleed",
+    "motion_blur": "Motion blur",
+    "gaussian_blur": "Gaussian blur",
+    "perspective": "Perspective warp",
+    "contrast": "Contrast shift",
+    "gamma": "Gamma shift",
+    "coffee_stain": "Coffee stain",
+    "stain": "Water / paper stain",
+    "shadow": "Shadow",
+    "crumple": "Crumple / wrinkle",
+    "discoloration": "Discoloration",
+    "page_tilt": "Page tilt",
+    "dust": "Dust specks",
+    "scanner_streaks": "Scanner streaks",
+    "scanner_noise": "Scanner noise",
+    "salt_pepper": "Salt & pepper noise",
+    "jpeg_compression": "JPEG compression",
+}
+
+
+def base_overrides() -> list[str]:
+    return [
+        f"document.template={TEMPLATE_ID}",
+        "placement.rotation_deg.min=0", "placement.rotation_deg.max=0",
+        "placement.scale_factor.min=0.85", "placement.scale_factor.max=0.85",
+        "placement.x_offset=0", "placement.y_offset=0",
+        "placement.misplacement_probability=0.0",
+        "generation.run_validation=false",
+    ]
+
+
+def all_disabled_overrides() -> list[str]:
+    overrides = base_overrides()
+    for name in ALL_EFFECTS:
+        overrides.append(f"augmentation.{name}.enabled=false")
+        overrides.append(f"augmentation.{name}.probability=0.0")
+    return overrides
+
+
+def effect_overrides(target_effect: str) -> list[str]:
+    overrides = base_overrides()
+    for name in ALL_EFFECTS:
+        is_target = name == target_effect
+        overrides.append(f"augmentation.{name}.enabled={'true' if is_target else 'false'}")
+        overrides.append(f"augmentation.{name}.probability={'1.0' if is_target else '0.0'}")
+    overrides.append(f"augmentation.{target_effect}.strength={FORCED_STRENGTH}")
+    return overrides
+
+
+def main() -> None:
+    OUT_ROOT.mkdir(parents=True, exist_ok=True)
+    manifest: dict[str, dict] = {}
+
+    baseline_config = load_config(overrides=all_disabled_overrides())
+    baseline_ctx = GeneratorContext(baseline_config)
+    baseline_meta, baseline_img, _ = baseline_ctx.run_sample(SAMPLE_INDEX)
+    box = baseline_meta["geometry"]["target_box"]
+
+    for effect in ALL_EFFECTS:
+        out_dir = OUT_ROOT / effect
+        out_dir.mkdir(parents=True, exist_ok=True)
+
+        config = load_config(overrides=effect_overrides(effect))
+        ctx = GeneratorContext(config)
+        meta, after_img, _ = ctx.run_sample(SAMPLE_INDEX)
+
+        before_img, after_img_final = baseline_img, after_img
+        if effect in SIGNATURE_EFFECTS:
+            before_out = _crop_region(before_img, box, pad=CROP_PAD)
+            after_out = _crop_region(after_img_final, box, pad=CROP_PAD)
+            before_pil = Image.fromarray(before_out)
+            after_pil = Image.fromarray(after_out)
+            before_pil = before_pil.resize(
+                (before_pil.width * CROP_UPSCALE, before_pil.height * CROP_UPSCALE), Image.LANCZOS,
+            )
+            after_pil = after_pil.resize(
+                (after_pil.width * CROP_UPSCALE, after_pil.height * CROP_UPSCALE), Image.LANCZOS,
+            )
+        else:
+            before_pil, after_pil = Image.fromarray(before_img), Image.fromarray(after_img_final)
+
+        before_pil.save(out_dir / "before.png")
+        after_pil.save(out_dir / "after.png")
+
+        if effect in ZOOM_EFFECTS:
+            x1, y1, x2, y2 = ZOOM_BOX
+            zb = Image.fromarray(before_img[y1:y2, x1:x2])
+            za = Image.fromarray(after_img_final[y1:y2, x1:x2])
+            zb = zb.resize((zb.width * CROP_UPSCALE, zb.height * CROP_UPSCALE), Image.LANCZOS)
+            za = za.resize((za.width * CROP_UPSCALE, za.height * CROP_UPSCALE), Image.LANCZOS)
+            zb.save(out_dir / "before_zoom.png")
+            za.save(out_dir / "after_zoom.png")
+
+        rec = meta["augmentation"]["effects"].get(effect, {})
+        manifest[effect] = {
+            "display_name": DISPLAY_NAME[effect],
+            "category": "signature" if effect in SIGNATURE_EFFECTS else "document",
+            "strength": rec.get("strength"),
+            "params": rec.get("params"),
+            "difficulty_score": meta["difficulty"]["score"],
+            "difficulty_tier": meta["difficulty"]["tier"],
+        }
+        print(f"[{effect}] before/after -> {out_dir}  (strength={rec.get('strength')}, params={rec.get('params')})")
+
+    manifest_path = OUT_ROOT / "before_after_manifest.json"
+    manifest_path.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
+    print(f"\nWrote manifest -> {manifest_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_case_assets.py b/unified_pipeline/scripts/generate_case_assets.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e76cae1e0e5b611cfaaf21cfcb43ff5434e92fd
--- /dev/null
+++ b/unified_pipeline/scripts/generate_case_assets.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+"""Generate plain full-page images (no dashboard overlay) for normal vs. extreme
+multi-degradation cases, for slides.
+
+Reuses the same two configurations as scripts/generate_multiple_degradation_gallery.py
+("normal": configs/generate.yaml's realistic per-effect probabilities, unmodified)
+and scripts/generate_multiple_degradation_extreme_gallery.py ("extreme": every
+effect pushed to high probability/strength plus worst-case placement/rendering),
+but writes the bare rendered page instead of the multi-panel debug figure, since
+slides just need the document image plus a short caption.
+
+Usage:
+    python scripts/generate_case_assets.py
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from PIL import Image
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from scripts.generate_multiple_degradation_extreme_gallery import build_overrides as extreme_overrides
+
+ROOT = Path(__file__).resolve().parents[1]
+TEMPLATE_ID = "bank_doc_05"
+N_SAMPLES = 2
+
+
+def _run_case(out_dir: Path, config_path, overrides: list[str]) -> None:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    config = load_config(config_path=config_path, overrides=overrides)
+    ctx = GeneratorContext(config)
+
+    manifest = {}
+    for i in range(1, N_SAMPLES + 1):
+        meta, final_image, _ = ctx.run_sample(i)
+        Image.fromarray(final_image).save(out_dir / f"case_{i}.png")
+
+        effects = meta["augmentation"]["effects"]
+        applied = [n for n, r in effects.items() if r["applied"]]
+        manifest[f"case_{i}"] = {
+            "applied_effects": applied,
+            "n_applied": len(applied),
+            "n_total": len(effects),
+            "difficulty_score": meta["difficulty"]["score"],
+            "difficulty_tier": meta["difficulty"]["tier"],
+            "placement": {
+                "rotation_deg": meta["placement"]["rotation_deg"],
+                "scale_factor": meta["placement"]["scale_factor"],
+                "misplaced": meta["placement"]["misplaced"],
+            },
+        }
+        print(f"[{out_dir.parent.name}/case_{i}] {len(applied)}/{len(effects)} effects, "
+              f"difficulty={meta['difficulty']['score']:.3f} ({meta['difficulty']['tier']})")
+
+    (out_dir / "case_manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8")
+
+
+def main() -> None:
+    # Normal: configs/generate.yaml's realistic "varied batch" probabilities, unmodified.
+    _run_case(
+        ROOT / "multiple_degradation" / "case_assets",
+        ROOT / "configs" / "generate.yaml",
+        [f"document.template={TEMPLATE_ID}", "generation.run_validation=false"],
+    )
+    # Extreme: every effect pushed high, worst-case placement/rendering.
+    _run_case(
+        ROOT / "multiple_degradation_extreme" / "case_assets",
+        None,
+        extreme_overrides(),
+    )
+    print("\nDone.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_degradation_gallery.py b/unified_pipeline/scripts/generate_degradation_gallery.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7164ab1d93fb3520f642e6d5cc44b49eea53ad9
--- /dev/null
+++ b/unified_pipeline/scripts/generate_degradation_gallery.py
@@ -0,0 +1,78 @@
+#!/usr/bin/env python
+"""Generate a labeled sample gallery of every augmentation effect in isolation.
+
+For each of the 25 configurable augmentation families, forces that one effect
+on (probability=1.0) and disables every other effect, so the visual result is
+attributable to that effect alone. Placement is frozen (no rotation/scale/
+misplacement jitter, fixed template) so samples across effects are directly
+comparable. Writes 5 sample images per effect to degradation/<effect>/, each
+a multi-panel figure (full page, signature crop, ink mask, coverage metrics,
+difficulty breakdown) via the existing visualizer.
+
+Usage:
+    python scripts/generate_degradation_gallery.py
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.visualization.visualizer import save_visualization
+
+SIGNATURE_EFFECTS = [
+    "rotation", "stroke_dilation",
+    "ink_fading", "ink_bleed", "motion_blur", "gaussian_blur",
+]
+DOCUMENT_EFFECTS = [
+    "perspective", "contrast", "gamma", "coffee_stain", "stain",
+    "shadow", "crumple", "discoloration", "page_tilt", "dust", "scanner_streaks",
+    "scanner_noise", "salt_pepper", "jpeg_compression",
+]
+ALL_EFFECTS = SIGNATURE_EFFECTS + DOCUMENT_EFFECTS
+
+OUT_ROOT = Path(__file__).resolve().parents[1] / "degradation"
+SAMPLES_PER_EFFECT = 5
+TEMPLATE_ID = "bank_doc_05"  # decent-sized signature box (255x35px)
+FORCED_STRENGTH = 0.75
+
+
+def build_overrides(target_effect: str) -> list[str]:
+    overrides = [
+        f"document.template={TEMPLATE_ID}",
+        "placement.rotation_deg.min=0", "placement.rotation_deg.max=0",
+        "placement.scale_factor.min=0.85", "placement.scale_factor.max=0.85",
+        "placement.x_offset=0", "placement.y_offset=0",
+        "placement.misplacement_probability=0.0",
+        "generation.run_validation=false",
+    ]
+    for name in ALL_EFFECTS:
+        is_target = name == target_effect
+        overrides.append(f"augmentation.{name}.enabled={'true' if is_target else 'false'}")
+        overrides.append(f"augmentation.{name}.probability={'1.0' if is_target else '0.0'}")
+    overrides.append(f"augmentation.{target_effect}.strength={FORCED_STRENGTH}")
+    return overrides
+
+
+def main() -> None:
+    OUT_ROOT.mkdir(parents=True, exist_ok=True)
+    for effect in ALL_EFFECTS:
+        out_dir = OUT_ROOT / effect
+        out_dir.mkdir(parents=True, exist_ok=True)
+
+        config = load_config(overrides=build_overrides(effect))
+        ctx = GeneratorContext(config)
+
+        for i in range(1, SAMPLES_PER_EFFECT + 1):
+            metadata, final_image, _ = ctx.run_sample(i)
+            save_visualization(out_dir / f"sample_{i}.png", final_image, metadata)
+
+        print(f"[{effect}] wrote {SAMPLES_PER_EFFECT} samples -> {out_dir}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_multiple_degradation_extreme_gallery.py b/unified_pipeline/scripts/generate_multiple_degradation_extreme_gallery.py
new file mode 100644
index 0000000000000000000000000000000000000000..f57d641f951606d7fdf1f315e3b27d3de8c22f04
--- /dev/null
+++ b/unified_pipeline/scripts/generate_multiple_degradation_extreme_gallery.py
@@ -0,0 +1,169 @@
+#!/usr/bin/env python
+"""Generate WORST-CASE samples: many degradations stacked at high strength on one template.
+
+Like scripts/generate_multiple_degradation_gallery.py, but every augmentation
+family's probability and strength range are pushed high (instead of using
+configs/generate.yaml's mild "realistic batch" defaults), and placement/
+rendering are also pushed toward their hardest settings (heavy misplacement,
+small/faint signature). Most effects fire on most samples, so each sample
+ends up with a large, clearly-visible stack of simultaneous degradations.
+
+The caption band at the top of each image lists not just which effects
+fired but their resolved *extent*: strength, plus any effect-specific
+parameter (blob count, shadow type, JPEG quality, wrinkle count/amplitude,
+etc.) alongside the resolved placement/rendering severity for that sample.
+
+Usage:
+    python scripts/generate_multiple_degradation_extreme_gallery.py
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from PIL import Image, ImageDraw, ImageFont
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.visualization.visualizer import save_visualization
+from scripts.generate_degradation_gallery import ALL_EFFECTS
+
+ROOT = Path(__file__).resolve().parents[1]
+OUT_DIR = ROOT / "multiple_degradation_extreme"
+N_SAMPLES = 10
+TEMPLATE_ID = "bank_doc_05"
+
+EFFECT_PROBABILITY = 0.85   # near-certain per effect, but still leaves sample-to-sample variety
+STRENGTH_MIN, STRENGTH_MAX = 0.7, 1.0
+
+
+def _load_font(size: int):
+    for candidate in (r"C:\Windows\Fonts\consola.ttf", r"C:\Windows\Fonts\arial.ttf"):
+        if Path(candidate).exists():
+            return ImageFont.truetype(candidate, size)
+    return ImageFont.load_default()
+
+
+def build_overrides() -> list[str]:
+    overrides = [
+        f"document.template={TEMPLATE_ID}",
+        "generation.run_validation=false",
+        # Worst-case placement: large rotation, small scale, frequent/strong misplacement.
+        "placement.rotation_deg.min=-15", "placement.rotation_deg.max=15",
+        "placement.scale_factor.min=0.3", "placement.scale_factor.max=0.6",
+        "placement.margin=0.03",
+        "placement.misplacement_probability=0.5",
+        "placement.misplacement_strength.min=0.5", "placement.misplacement_strength.max=1.0",
+        "placement.allow_partial_outside=true",
+        # Worst-case rendering: faint, blurred, heavily feathered ink.
+        "rendering.ink_opacity.min=0.3", "rendering.ink_opacity.max=0.55",
+        "rendering.blur_sigma.min=0.6", "rendering.blur_sigma.max=1.4",
+        "rendering.feather_px.min=1.5", "rendering.feather_px.max=3.0",
+        "rendering.scanner_noise=0.05",
+    ]
+    for name in ALL_EFFECTS:
+        overrides.append(f"augmentation.{name}.enabled=true")
+        overrides.append(f"augmentation.{name}.probability={EFFECT_PROBABILITY}")
+        if name == "jpeg_compression":
+            overrides += [
+                "augmentation.jpeg_compression.strength=0.9",
+                "augmentation.jpeg_compression.min_quality=10",
+                "augmentation.jpeg_compression.max_quality=30",
+            ]
+        else:
+            overrides.append(f"augmentation.{name}.strength.min={STRENGTH_MIN}")
+            overrides.append(f"augmentation.{name}.strength.max={STRENGTH_MAX}")
+    return overrides
+
+
+def _fmt_effect(name: str, rec: dict) -> str:
+    params = rec.get("params") or {}
+    if name == "jpeg_compression" and "quality" in params:
+        return f"{name}=q{params['quality']}"
+    tag = f"{name}={rec['strength']:.2f}"
+    if "resolved_type" in params:
+        tag += f"({params['resolved_type']})"
+    elif "resolved_tone" in params:
+        tag += f"({params['resolved_tone']})"
+    elif name == "crumple" and "wrinkle_lines" in params:
+        tag += f"({params['wrinkle_lines']}L,{params['displacement_amplitude_px']:.1f}px)"
+    elif name == "coffee_stain" and "blob_count" in params:
+        tag += f"({params['blob_count']}blobs)"
+    elif name == "page_tilt" and "tilt_deg" in params:
+        tag += f"({params['tilt_deg']:.1f}deg)"
+    elif name == "dust" and "speck_count" in params:
+        tag += f"({params['speck_count']}px)"
+    elif name == "scanner_streaks" and "streak_count" in params:
+        tag += f"({params['streak_count']}streaks)"
+    return tag
+
+
+def _annotate(path: Path, metadata: dict) -> list[str]:
+    effects = metadata["augmentation"]["effects"]
+    applied_names = [n for n, r in effects.items() if r["applied"]]
+    applied_tags = [_fmt_effect(n, effects[n]) for n in applied_names]
+
+    placement, rendering, difficulty = metadata["placement"], metadata["rendering"], metadata["difficulty"]
+    header = (
+        f"{metadata['document_id']}  |  difficulty={difficulty['score']:.3f} ({difficulty['tier']})  |  "
+        f"placement: rot={placement['rotation_deg']:.1f}deg scale={placement['scale_factor']:.2f} "
+        f"misplaced={placement['misplaced']}  |  "
+        f"render: ink_opacity={rendering['ink_opacity']:.2f} blur_sigma={rendering['blur_sigma']:.2f}"
+    )
+    body = f"Applied ({len(applied_tags)}/{len(effects)}): " + (", ".join(applied_tags) if applied_tags else "none")
+
+    img = Image.open(path).convert("RGB")
+    font = _load_font(15)
+    draw_probe = ImageDraw.Draw(img)
+    max_w = img.width - 28
+
+    # Pixel-accurate wrap of the (potentially long) applied-effects line.
+    words = body.replace(", ", " ,SPLIT_ME ").split(" ,SPLIT_ME ")
+    lines, current = [], ""
+    for tok in words:
+        candidate = (current + ", " + tok) if current else tok
+        if draw_probe.textlength(candidate, font=font) <= max_w:
+            current = candidate
+        else:
+            if current:
+                lines.append(current)
+            current = tok
+    if current:
+        lines.append(current)
+
+    all_lines = [header] + lines
+    line_h = 22
+    band_h = 14 + line_h * len(all_lines)
+
+    canvas = Image.new("RGB", (img.width, img.height + band_h), "white")
+    canvas.paste(img, (0, band_h))
+    draw = ImageDraw.Draw(canvas)
+    draw.rectangle([0, 0, canvas.width, band_h], fill=(60, 18, 18))
+    y = 8
+    for i, line in enumerate(all_lines):
+        draw.text((14, y), line, fill="white" if i == 0 else (255, 214, 194), font=font)
+        y += line_h
+    canvas.save(path)
+    return applied_names
+
+
+def main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    config = load_config(overrides=build_overrides())
+    ctx = GeneratorContext(config)
+
+    for i in range(1, N_SAMPLES + 1):
+        metadata, final_image, _ = ctx.run_sample(i)
+        out_path = OUT_DIR / f"sample_{i}.png"
+        save_visualization(out_path, final_image, metadata)
+        applied = _annotate(out_path, metadata)
+        print(f"[sample_{i}] {len(applied)}/{len(metadata['augmentation']['effects'])} effects -> {', '.join(applied)}")
+
+    print(f"\nWrote {N_SAMPLES} extreme samples -> {OUT_DIR}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_multiple_degradation_gallery.py b/unified_pipeline/scripts/generate_multiple_degradation_gallery.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a7086a415668d6b06fc882076dcd68e21dbebe0
--- /dev/null
+++ b/unified_pipeline/scripts/generate_multiple_degradation_gallery.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+"""Generate samples with multiple random degradations stacked on one template.
+
+Unlike scripts/generate_degradation_gallery.py (which isolates exactly one
+effect per sample for comparison), this uses the realistic "varied batch"
+probabilities from configs/generate.yaml unmodified -- every enabled effect
+independently rolls its own probability per sample, so different samples end
+up with different *combinations* of simultaneously-applied degradations, all
+on the same template/box for a fair before/after comparison.
+
+Each output image is the standard metrics visualization (full page, crop,
+ink mask, coverage metrics, difficulty breakdown) plus a caption band listing
+exactly which effects fired for that sample.
+
+Usage:
+    python scripts/generate_multiple_degradation_gallery.py
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from PIL import Image, ImageDraw, ImageFont
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.visualization.visualizer import save_visualization
+
+ROOT = Path(__file__).resolve().parents[1]
+OUT_DIR = ROOT / "multiple_degradation"
+N_SAMPLES = 10
+TEMPLATE_ID = "bank_doc_05"  # same template used in the single-effect gallery
+
+
+def _load_font(size: int) -> "ImageFont.FreeTypeFont | ImageFont.ImageFont":
+    for candidate in (r"C:\Windows\Fonts\consola.ttf", r"C:\Windows\Fonts\arial.ttf"):
+        if Path(candidate).exists():
+            return ImageFont.truetype(candidate, size)
+    return ImageFont.load_default()
+
+
+def _annotate_applied_effects(path: Path, metadata: dict) -> list[str]:
+    effects = metadata["augmentation"]["effects"]
+    applied = [name for name, rec in effects.items() if rec["applied"]]
+    difficulty = metadata["difficulty"]
+
+    caption = (
+        f"{metadata['document_id']}  |  applied degradations "
+        f"({len(applied)}/{len(effects)}): {', '.join(applied) if applied else 'none'}"
+        f"   |   difficulty = {difficulty['score']:.3f} ({difficulty['tier']})"
+    )
+
+    img = Image.open(path).convert("RGB")
+    band_h = 40
+    canvas = Image.new("RGB", (img.width, img.height + band_h), "white")
+    canvas.paste(img, (0, band_h))
+    draw = ImageDraw.Draw(canvas)
+    font = _load_font(16)
+    draw.rectangle([0, 0, canvas.width, band_h], fill=(15, 42, 67))
+    draw.text((14, 11), caption, fill="white", font=font)
+    canvas.save(path)
+    return applied
+
+
+def main() -> None:
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    overrides = [
+        f"document.template={TEMPLATE_ID}",
+        "generation.run_validation=false",
+    ]
+    config = load_config(config_path=ROOT / "configs" / "generate.yaml", overrides=overrides)
+    ctx = GeneratorContext(config)
+
+    for i in range(1, N_SAMPLES + 1):
+        metadata, final_image, _ = ctx.run_sample(i)
+        out_path = OUT_DIR / f"sample_{i}.png"
+        save_visualization(out_path, final_image, metadata)
+        applied = _annotate_applied_effects(out_path, metadata)
+        print(f"[sample_{i}] {len(applied)} effects applied -> {', '.join(applied) or 'none'}")
+
+    print(f"\nWrote {N_SAMPLES} samples -> {OUT_DIR}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_showcase_dataset.py b/unified_pipeline/scripts/generate_showcase_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..93ebfb42c72447dc139bcd62531c67b42784d2af
--- /dev/null
+++ b/unified_pipeline/scripts/generate_showcase_dataset.py
@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+"""Generate the flagship showcase dataset: all 12 verification case types,
+each rendered once on a SCANNED template and once on a PROCEDURAL template
+-- 24 cases total, demonstrating every case type and both template families
+side by side in one clearly-organized, self-contained folder.
+
+Folder/file naming is deliberately verbose and self-describing (no need to
+open a JSON to know what a case is):
+
+    case_001_1a_scanned/
+        case_001_1a_scanned_document.png
+        case_001_1a_scanned_reference_signature.png
+        case_001_1a_scanned_ground_truth.json
+    case_002_1a_procedural/
+        ...
+    before_after/
+        case_001_1a_scanned_before_after.png
+        case_002_1a_procedural_before_after.png
+        ...   # every case's metrics preview, grouped in one folder --
+               # easier to flip through in a row than opening 24 case folders
+
+Usage:
+    python scripts/generate_showcase_dataset.py [--output data/showcase_dataset] [--seed-base 1000]
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import shutil
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext, _export_reference_signature, compute_extra_signature_overlays
+from src.utils.image_io import save_image_rgb
+from src.verification.case_types import CASE_TABLE
+from src.visualization.before_after_preview import save_before_after_preview
+
+ROOT = Path(__file__).resolve().parents[1]
+
+# Which base config each provider uses -- "procedural" pulls in the
+# recalibrated difficulty constants + cosmetic fields (configs/procedural_templates.yaml),
+# "scanned" uses the deck-literal defaults (configs/default.yaml). See
+# unified_pipeline/README.md's "Difficulty calibration profiles" section
+# for why these two profiles use different reference constants.
+PROVIDER_CONFIG = {
+    "scanned": None,  # None = configs/default.yaml only
+    "procedural": str(ROOT / "configs" / "procedural_templates.yaml"),
+}
+
+
+def _record_signer(
+    index: "dict[str, dict]", signer_group: "str | None", display_name: "str | None", script: "str | None",
+    role: "str | None", case_id: str, template_id: str, kind: str, ref_path: "str | None",
+) -> None:
+    """Adds one (case, box) appearance to the cross-case reference-signer
+    index -- answers "which name/role is this signature actually of, and
+    where else does that same person's ink appear in this dataset" without
+    having to cross-reference every case folder's ground_truth.json by hand.
+    No-op for boxes with no real signer (blank/printed-text cases)."""
+    if not signer_group:
+        return
+    entry = index.setdefault(signer_group, {
+        "signer_group": signer_group, "display_name": display_name, "script": script,
+        "roles": [], "appears_in": [], "reference_images": [],
+    })
+    if role and role not in entry["roles"]:
+        entry["roles"].append(role)
+    entry["appears_in"].append({"case_id": case_id, "role": role, "as": kind, "template": template_id})
+    if ref_path:
+        ref_path = ref_path.replace("\\", "/")  # some source paths come through OS-native, some already posix
+        if ref_path not in entry["reference_images"]:
+            entry["reference_images"].append(ref_path)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("--output", default=str(ROOT / "data" / "showcase_dataset"))
+    parser.add_argument("--seed-base", type=int, default=1000)
+    args = parser.parse_args()
+
+    out_dir = Path(args.output)
+    if out_dir.exists():
+        shutil.rmtree(out_dir)
+    out_dir.mkdir(parents=True)
+
+    rows = []
+    signer_index: "dict[str, dict]" = {}
+    i = 0
+    for code, spec in CASE_TABLE.items():
+        for provider in ("scanned", "procedural"):
+            i += 1
+            case_id = f"case_{i:03d}_{code}_{provider}"
+            seed = args.seed_base + i
+            config = load_config(
+                config_path=PROVIDER_CONFIG[provider],
+                overrides=[
+                    f"seed={seed}",
+                    f"quality.case_type={code}",
+                    f"paths.template_provider={provider}",
+                    "document.enable_cosmetic_fields=true",
+                ],
+            )
+            ctx = GeneratorContext(config)
+            metadata, final_image, validation_report = ctx.run_sample(1)
+            metadata["case_id"] = case_id  # overwrite the generic sample_000001-style id with the showcase name
+
+            case_dir = out_dir / case_id
+            case_dir.mkdir()
+            save_image_rgb(case_dir / f"{case_id}_document.png", final_image)
+
+            v = metadata["verification"]
+            sig_source = metadata.get("signature_source") or {}
+            if sig_source.get("path"):
+                _export_reference_signature(sig_source["path"], case_dir / f"{case_id}_reference_signature.png")
+                _record_signer(
+                    signer_index, v["actual_signer_group"], metadata["signer"]["name"], v["actual_signer_script"],
+                    metadata["signer"]["role"], case_id, metadata["template"], "primary", sig_source["path"],
+                )
+            companion = (metadata.get("verification") or {}).get("companion") or {}
+            if companion.get("ref_path"):
+                _export_reference_signature(companion["ref_path"], case_dir / f"{case_id}_companion_signature.png")
+                companion_box = next((b for b in ctx.template_provider.get(metadata["template"]).annotation.pages[0].signature_boxes if b.id == companion["box_id"]), None)
+                _record_signer(
+                    signer_index, companion["signer_group"], ctx.identity_registry.display_name(companion["signer_group"]),
+                    companion["script"], companion_box.role if companion_box else None, case_id, metadata["template"], "companion", companion["ref_path"],
+                )
+            for extra in metadata.get("additional_signatures") or []:
+                _export_reference_signature(extra["ref_path"], case_dir / f"{case_id}_{extra['box_id']}_signature.png")
+                _record_signer(
+                    signer_index, extra["signer_group"], ctx.identity_registry.display_name(extra["signer_group"]),
+                    extra["script"], extra["role"], case_id, metadata["template"], "additional", extra["ref_path"],
+                )
+
+            ctx.metadata_exporter.export(metadata, case_dir / f"{case_id}_ground_truth.json")
+
+            if ctx.last_clean_image is not None:
+                # Grouped in one shared folder rather than scattered one-per-case
+                # -- easier to flip through every case's metrics preview in a
+                # row without opening each case folder individually.
+                extra_boxes = compute_extra_signature_overlays(ctx, metadata, final_image)
+                save_before_after_preview(
+                    out_dir / "before_after" / f"{case_id}_before_after.png", ctx.last_clean_image, final_image,
+                    metadata, config.difficulty, extra_boxes,
+                )
+
+            passed = validation_report is not None and validation_report.passed
+            rows.append({
+                "case_id": case_id, "case_type": code, "description": spec.description,
+                "template_provider": provider, "template_id": metadata["template"],
+                "verdict": v["verdict"], "reject_code": v["reject_code"] or "",
+                "signer_name": metadata["signer"]["name"], "signer_script": v["expected_signer_script"],
+                "difficulty_tier": metadata["difficulty"]["tier"], "difficulty_score": metadata["difficulty"]["score"],
+                "validation_passed": passed,
+            })
+            print(f"[{case_id}] template={metadata['template']:<12} verdict={v['verdict']:<7} "
+                  f"{v['reject_code'] or '-':<24} difficulty={metadata['difficulty']['tier']} "
+                  f"({'OK' if passed else 'VALIDATION FAILED'})")
+
+    index_path = out_dir / "SHOWCASE_INDEX.csv"
+    with index_path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(rows)
+
+    # Cross-case identity index: every name/role/entity that appears
+    # anywhere in this dataset, with which case(s) it appears in and
+    # which actual reference signature image(s) back it -- so "who is
+    # 'John Thompson' and where does his ink come from" is one lookup
+    # instead of grepping every case folder's ground_truth.json by hand.
+    signers_path = out_dir / "reference_signers_index.json"
+    signers_path.write_text(
+        json.dumps(sorted(signer_index.values(), key=lambda e: e["display_name"] or ""), indent=2), encoding="utf-8",
+    )
+
+    n_failed = sum(1 for r in rows if not r["validation_passed"])
+    print(f"\nWrote {len(rows)} cases ({len(rows) // 2} case types x 2 template providers) to {out_dir}")
+    print(f"Index: {index_path}")
+    print(f"Reference-signer index: {signers_path} ({len(signer_index)} distinct signer(s))")
+    if n_failed:
+        print(f"WARNING: {n_failed} case(s) failed validation -- check the log above.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/scripts/generate_verification_suite.py b/unified_pipeline/scripts/generate_verification_suite.py
new file mode 100644
index 0000000000000000000000000000000000000000..848e09cea391b09076931e442e16c69bbd5cb6e0
--- /dev/null
+++ b/unified_pipeline/scripts/generate_verification_suite.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+"""Generate a labeled sample batch covering all 12 verification test-case types.
+
+For each ``case_type`` in ``src.verification.case_types.CASE_TABLE`` (1a
+through 5b), generates a couple of samples via ``GeneratorContext`` directly
+(bypassing ``DatasetGenerator`` since each case needs its own config
+override), writes the rendered PNGs + metadata JSONs to
+``output/verification_suite/``, and prints a verdict/reject_code summary
+table.
+
+Usage:
+    python scripts/generate_verification_suite.py [--per-case N]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.metadata.metadata_exporter import MetadataExporter
+from src.utils.image_io import save_image_rgb
+from src.verification.case_types import CASE_TABLE
+
+ROOT = Path(__file__).resolve().parents[1]
+OUT_DIR = ROOT / "output" / "verification_suite"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--per-case", type=int, default=2, help="Samples to generate per case type.")
+    parser.add_argument("--seed", type=int, default=4242, help="Master seed.")
+    args = parser.parse_args()
+
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    exporter = MetadataExporter()
+    summary_rows = []
+
+    sample_index = 1
+    for code in CASE_TABLE:
+        config = load_config(
+            config_path=str(ROOT / "configs" / "examples" / "verification_cases.yaml"),
+            overrides=[f"quality.case_type={code}", f"seed={args.seed}"],
+        )
+        ctx = GeneratorContext(config)
+
+        for _ in range(args.per_case):
+            sample_id = f"case_{sample_index:03d}"
+            metadata, final_image, _ = ctx.run_sample(sample_index)
+            metadata["document_id"] = sample_id
+
+            save_image_rgb(OUT_DIR / f"{sample_id}.png", final_image)
+            exporter.export(metadata, OUT_DIR / f"{sample_id}.json")
+
+            v = metadata["verification"]
+            summary_rows.append({
+                "sample_id": sample_id, "case_type": code, "template": metadata["template"],
+                "box_id": v["target_box_id"], "verdict": v["verdict"], "reject_code": v["reject_code"],
+            })
+            print(f"[{sample_id}] case_type={code:<3} template={metadata['template']:<12} "
+                  f"box={v['target_box_id']:<5} -> {v['verdict']:<7} {v['reject_code'] or '-'}")
+            sample_index += 1
+
+    (OUT_DIR / "summary.json").write_text(json.dumps(summary_rows, indent=2), encoding="utf-8")
+    print(f"\nWrote {len(summary_rows)} samples to {OUT_DIR}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/unified_pipeline/src/__init__.py b/unified_pipeline/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8558c6a061cdaa2f95c0a1b21a0424909a17e049
--- /dev/null
+++ b/unified_pipeline/src/__init__.py
@@ -0,0 +1,3 @@
+"""Synthetic signed bank document generation pipeline."""
+
+__version__ = "1.0.0"
diff --git a/unified_pipeline/src/analysis/__init__.py b/unified_pipeline/src/analysis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..87a06c3e4f150c93b5862d13dc24699ded38ee76
--- /dev/null
+++ b/unified_pipeline/src/analysis/__init__.py
@@ -0,0 +1 @@
+"""Post-render geometry detection, difficulty scoring, and validation."""
diff --git a/unified_pipeline/src/analysis/difficulty_analyzer.py b/unified_pipeline/src/analysis/difficulty_analyzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fc1b95dadc05cc0c724dc409f85f369a9fe2741
--- /dev/null
+++ b/unified_pipeline/src/analysis/difficulty_analyzer.py
@@ -0,0 +1,81 @@
+"""Computes the deterministic difficulty score from post-render geometry metrics.
+
+Pure function of :class:`~src.analysis.geometry_analyzer.GeometryResult` and
+the configured weights -- no randomness is involved, so the score is
+trivially reproducible given identical geometry inputs.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+from src.analysis.geometry_analyzer import GeometryResult
+from src.config.schema import DifficultyWeights
+from src.utils.geometry_utils import clamp01
+
+
+@dataclass
+class DifficultyResult:
+    """The five difficulty components, the weighted score, and its tier."""
+
+    misplacement: float
+    whitespace: float
+    faintness: float
+    smallness: float
+    low_texture: float
+    score: float
+    tier: str
+
+
+class DifficultyAnalyzer:
+    """Computes the exact spec-defined difficulty score and tier.
+
+    ``score = 0.35*misplacement + 0.20*whitespace + 0.20*faintness + 0.15*smallness + 0.10*low_texture``
+
+    Example:
+        >>> analyzer = DifficultyAnalyzer(cfg.difficulty)
+        >>> result = analyzer.analyze(geometry_result)
+    """
+
+    def __init__(self, weights: DifficultyWeights) -> None:
+        self.weights = weights
+
+    def analyze(self, geometry: GeometryResult) -> DifficultyResult:
+        """Compute the difficulty components, weighted score, and tier for one sample."""
+        w = self.weights
+
+        faintness_range = w.faintness_light_ref - w.faintness_dark_ref
+
+        misplacement = clamp01(1.0 - geometry.polygon_overlap_proxy)
+        whitespace = clamp01(geometry.whitespace_ratio)
+        faintness = clamp01((geometry.mean_dark_intensity - w.faintness_dark_ref) / faintness_range)
+        smallness = clamp01(1.0 - geometry.signature_area_ratio / w.smallness_reference_area_ratio)
+        low_texture = clamp01(1.0 - geometry.gray_stddev / w.low_texture_reference_stddev)
+
+        score = (
+            w.misplacement * misplacement
+            + w.whitespace * whitespace
+            + w.faintness * faintness
+            + w.smallness * smallness
+            + w.low_texture * low_texture
+        )
+        score = float(np.clip(score, 0.0, 1.0))
+
+        if score < w.easy_threshold:
+            tier = "easy"
+        elif score < w.hard_threshold:
+            tier = "medium"
+        else:
+            tier = "hard"
+
+        return DifficultyResult(
+            misplacement=round(misplacement, 4),
+            whitespace=round(whitespace, 4),
+            faintness=round(faintness, 4),
+            smallness=round(smallness, 4),
+            low_texture=round(low_texture, 4),
+            score=round(score, 4),
+            tier=tier,
+        )
diff --git a/unified_pipeline/src/analysis/geometry_analyzer.py b/unified_pipeline/src/analysis/geometry_analyzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..92aef6a529b771cec1ebc2498339baf3c860ddb0
--- /dev/null
+++ b/unified_pipeline/src/analysis/geometry_analyzer.py
@@ -0,0 +1,402 @@
+"""Post-render signature detection and geometry/coverage metric computation.
+
+Rather than searching the entire page (which would pick up printed field
+labels like "By:" or "Print Name:"), detection is scoped to the known
+signature field location -- exactly as a production pipeline would use the
+document's layout annotation to know where to look. A tight pass is tried
+first; if no ink is found (e.g. the signature was intentionally misplaced
+far from its box) the search window progressively expands, bounded by the
+page, before giving up.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+import cv2
+import numpy as np
+
+from src.utils.geometry_utils import (
+    Box, box_center, box_to_polygon, centroid_of, containment_pct,
+    coverage_pct, distance_between, iou, margin_utilization, points_to_polygon, safe_area,
+)
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+MIN_INK_PIXELS = 12
+
+
+@dataclass
+class GeometryResult:
+    """Full geometric + photometric characterization of the detected signature."""
+
+    found: bool
+    signature_bbox_px: Box
+    min_rotated_rect: dict
+    signature_polygon_px: list[list[float]]
+    convex_hull_px: list[list[float]]
+    # One (or more, if the ink broke into disjoint blobs) real per-component
+    # boundary contour(s) -- for VISUALIZATION only (see
+    # src/visualization/before_after_preview.py), never used in scoring.
+    # signature_polygon_px/convex_hull_px above are the CONVEX HULL of every
+    # ink pixel (matches projectA's own computation, including its
+    # approxPolyDP simplification), correct for coverage/IoU/containment
+    # math but "bridges" gaps between separate ink blobs (e.g. between
+    # letters, or a flourish far from the main strokes) with straight
+    # lines -- confirmed, on direct user feedback, to still visibly fail
+    # to "hug" a signature with a real gap even after matching projectA's
+    # exact rendering (that's projectA's own known tradeoff, just not
+    # visible on a compact signature). These contours hug each real ink
+    # blob's own boundary instead, so a translucent fill of them only ever
+    # tints actual ink, never the whitespace between separate strokes.
+    display_contours: list[list[list[float]]]
+    centroid_px: tuple[float, float]
+    orientation_deg: float
+    ink_pixels: int
+    target_box: Box
+    actual_box: Box
+    search_box: Box
+
+    box_coverage_pct: float
+    containment_pct: float
+    ink_fill_ratio: float
+    polygon_overlap_proxy: float
+    distance_from_box_center_px: float
+    margin_utilization: float
+
+    mean_dark_intensity: float
+    gray_stddev: float
+    signature_area_ratio: float
+    whitespace_ratio: float
+
+
+def _expand_box(box: Box, canvas_size: tuple[int, int], factor: float, min_pad: float) -> Box:
+    x1, y1, x2, y2 = box
+    w, h = canvas_size
+    bw, bh = (x2 - x1), (y2 - y1)
+    pad_x = max(bw * (factor - 1.0) / 2.0, min_pad)
+    pad_y = max(bh * (factor - 1.0) / 2.0, min_pad)
+    return (
+        float(max(x1 - pad_x, 0)), float(max(y1 - pad_y, 0)),
+        float(min(x2 + pad_x, w)), float(min(y2 + pad_y, h)),
+    )
+
+
+def _union_box(a: Box, b: Box) -> Box:
+    return (min(a[0], b[0]), min(a[1], b[1]), max(a[2], b[2]), max(a[3], b[3]))
+
+
+def _is_printed_rule_line(width: int, height: int, area: int) -> bool:
+    """Heuristic filter for printed "sign here" guideline rules vs. handwritten ink.
+
+    Printed rules are thin (<=3px tall), very wide relative to their height,
+    and nearly solid (high fill ratio) -- unlike handwriting, which varies in
+    stroke height and leaves gaps even in a single connected component.
+    """
+    if height > 3:
+        return False
+    if width < 8 * max(height, 1):
+        return False
+    fill_ratio = area / max(width * height, 1)
+    return fill_ratio > 0.6
+
+
+def _strip_printed_rule_line_pixels(binary: np.ndarray) -> np.ndarray:
+    """Removes long, unbroken horizontal-line pixels (the template's own
+    printed "sign here" rule) from an ink mask BEFORE connected-component
+    analysis groups pixels together.
+
+    A pasted signature's ink very often sits directly on top of, and
+    touches, that printed line -- when it does, plain 8-connectivity merges
+    the line and the ink into ONE connected component, whose bounding box
+    then spans the printed line's full width (often the whole box) instead
+    of just where the ink actually is. `_is_printed_rule_line()` below
+    (per-component, post-hoc) only catches a line that stayed its own
+    separate component; it can't un-merge one that's already fused with
+    real ink. A wide horizontal morphological opening isolates only pixels
+    that are part of a long, uninterrupted straight run -- true of a
+    printed rule, essentially never true of handwriting for more than a
+    few pixels at a time (natural slant/curviness breaks a single-row run
+    quickly) -- so subtracting it erases the line's own pixels while
+    leaving the ink's pixels, even where they touched, intact. Deliberately
+    a small ABSOLUTE width, not a fraction of the crop: once a signature
+    crosses the middle of the line, the line survives as two SHORTER
+    sub-segments on either side of the crossing, and a proportionally-sized
+    kernel could exceed either sub-segment's length, letting both slip
+    through undetected.
+
+    The 1-row-tall opening kernel only checks each row in isolation, so a
+    genuinely TALL solid block of ink (thick handwriting, or a filled test
+    fixture) satisfies the same "long unbroken run" test on every one of
+    its rows and would otherwise be erased in full, row by row -- that's
+    not what a printed rule looks like (``_is_printed_rule_line()``
+    elsewhere in this module defines one as <=3px tall). So candidate line
+    pixels are re-checked per connected-component afterward: only ones
+    whose overall bounding height stays <=3px are actually subtracted.
+    """
+    h, w = binary.shape[:2]
+    kernel_w = 20
+    if kernel_w >= w:
+        return binary
+    line_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_w, 1))
+    line_pixels = cv2.morphologyEx(binary, cv2.MORPH_OPEN, line_kernel, iterations=1)
+    if line_pixels.sum() == 0:
+        return binary
+
+    n_labels, labels, stats, _ = cv2.connectedComponentsWithStats(line_pixels, connectivity=8)
+    thin_line_pixels = np.zeros_like(line_pixels)
+    for label in range(1, n_labels):
+        if stats[label, cv2.CC_STAT_HEIGHT] <= 3:
+            thin_line_pixels[labels == label] = 255
+    return cv2.bitwise_and(binary, cv2.bitwise_not(thin_line_pixels))
+
+
+def _mask_already_dark_in_baseline(binary: np.ndarray, gray_crop: np.ndarray, baseline_crop: np.ndarray) -> np.ndarray:
+    """Removes pixels that were ALREADY dark in the clean, unsigned template
+    (printed field labels like "Bank Manager:", box borders, etc.) from an
+    ink mask -- these sit right next to, and sometimes touch, a short
+    signature box's search region, and get picked up as "ink" by simple
+    brightness thresholding otherwise. The clean template image is known in
+    advance (the same way a real production pipeline knows its own form
+    layouts, not the specific run's signature placement) so this isn't
+    using ground truth the way a real verifier couldn't -- it's exactly
+    what :meth:`_strip_printed_rule_line_pixels` already does for straight
+    rule lines, generalized to any pre-existing printed content shape."""
+    if baseline_crop.shape != gray_crop.shape:
+        return binary
+    baseline_background = float(np.percentile(baseline_crop, 90))
+    baseline_threshold = baseline_background - max(18.0, baseline_background * 0.15)
+    baseline_dark = baseline_crop.astype(np.float32) < baseline_threshold
+    out = binary.copy()
+    out[baseline_dark] = 0
+    return out
+
+
+def _ink_mask(gray_crop: np.ndarray, baseline_crop: "np.ndarray | None" = None) -> "np.ndarray | None":
+    if gray_crop.size == 0:
+        return None
+    background = float(np.percentile(gray_crop, 90))
+    threshold = background - max(18.0, background * 0.15)
+    binary = (gray_crop.astype(np.float32) < threshold).astype(np.uint8) * 255
+    if binary.sum() == 0:
+        return binary
+    binary = _strip_printed_rule_line_pixels(binary)
+    if baseline_crop is not None:
+        binary = _mask_already_dark_in_baseline(binary, gray_crop, baseline_crop)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
+    opened = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
+    n_labels, labels, stats, _ = cv2.connectedComponentsWithStats(opened, connectivity=8)
+    cleaned = np.zeros_like(opened)
+    for label in range(1, n_labels):
+        area = stats[label, cv2.CC_STAT_AREA]
+        w = stats[label, cv2.CC_STAT_WIDTH]
+        h = stats[label, cv2.CC_STAT_HEIGHT]
+        if area < 2:
+            continue
+        if _is_printed_rule_line(w, h, area):
+            continue
+        cleaned[labels == label] = 255
+    return cleaned
+
+
+class GeometryAnalyzer:
+    """Detects the rendered signature within its known field and computes all geometry metrics.
+
+    Example:
+        >>> analyzer = GeometryAnalyzer()
+        >>> result = analyzer.analyze(final_image, target_box=(100, 100, 300, 160),
+        ...                           canvas_size=(1024, 559))
+    """
+
+    def analyze(
+        self,
+        image_rgb: np.ndarray,
+        target_box: Box,
+        canvas_size: tuple[int, int],
+        placement_hint_bbox: "Box | None" = None,
+        baseline_image_rgb: "np.ndarray | None" = None,
+        expect_blank: bool = False,
+    ) -> GeometryResult:
+        """Detect the rendered signature and compute every geometry/coverage metric.
+
+        Args:
+            image_rgb: The fully rendered (and augmented) document, ``(H, W, 3)`` uint8.
+            target_box: The annotated ``(x1, y1, x2, y2)`` signature field.
+            canvas_size: ``(width, height)`` of the document.
+            placement_hint_bbox: Optional pre-augmentation placement bounding
+                box, used to widen the search if a tight pass finds nothing
+                (e.g. after a large intentional misplacement).
+            baseline_image_rgb: Optional clean, unsigned template image
+                (same canvas size, before any signature/cosmetic text was
+                drawn) -- when given, pixels that were already dark in the
+                template itself (printed field labels, box borders) are
+                excluded from ink detection, so a signature box search
+                region that happens to sit close to printed text doesn't
+                pick that text up as "ink" (see
+                :func:`_mask_already_dark_in_baseline`). Not used if the
+                sample underwent a geometric document-level transform
+                (rotation/tilt/perspective) that could misalign it against
+                ``image_rgb`` -- caller's responsibility to omit it then.
+            expect_blank: Set when the caller already knows (from ground
+                truth, e.g. a 2a/5a case's target box) that NO signature was
+                placed here. Skips the second, widened retry pass entirely
+                -- that retry exists to chase a signature that was
+                deliberately misplaced far from its own box, not to search
+                a box we already know is empty. Without this, on a sample
+                where ``baseline_image_rgb`` had to be omitted (a page-level
+                geometric transform was applied, so a pixel diff would be
+                misaligned), the widened pass had nothing to exclude
+                already-printed content with, and could pick up a nearby
+                printed caption/label below the blank box as if it were a
+                wildly misplaced signature.
+
+        Returns:
+            A :class:`GeometryResult` with detection status and every metric.
+        """
+        gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
+        baseline_gray = cv2.cvtColor(baseline_image_rgb, cv2.COLOR_RGB2GRAY) if baseline_image_rgb is not None else None
+
+        search_box = _expand_box(target_box, canvas_size, factor=1.15, min_pad=6)
+        mask, offset = self._try_detect(gray, search_box, baseline_gray)
+
+        if not expect_blank and (mask is None or int((mask > 0).sum()) < MIN_INK_PIXELS):
+            union = _union_box(target_box, placement_hint_bbox) if placement_hint_bbox else target_box
+            search_box = _expand_box(union, canvas_size, factor=2.2, min_pad=50)
+            mask, offset = self._try_detect(gray, search_box, baseline_gray)
+
+        if mask is None or int((mask > 0).sum()) < MIN_INK_PIXELS:
+            if not expect_blank:
+                logger.warning("No signature ink detected near target box %s.", target_box)
+            return self._empty_result(target_box, search_box, canvas_size)
+
+        return self._build_result(mask, offset, gray, target_box, search_box, canvas_size)
+
+    @staticmethod
+    def _try_detect(
+        gray: np.ndarray, box: Box, baseline_gray: "np.ndarray | None" = None,
+    ) -> tuple["np.ndarray | None", tuple[int, int]]:
+        x1, y1, x2, y2 = (int(round(v)) for v in box)
+        crop = gray[y1:y2, x1:x2]
+        baseline_crop = baseline_gray[y1:y2, x1:x2] if baseline_gray is not None else None
+        mask = _ink_mask(crop, baseline_crop)
+        return mask, (x1, y1)
+
+    def _build_result(
+        self, mask: np.ndarray, offset: tuple[int, int], full_gray: np.ndarray,
+        target_box: Box, search_box: Box, canvas_size: tuple[int, int],
+    ) -> GeometryResult:
+        ox, oy = offset
+        ys, xs = np.where(mask > 0)
+        points = np.column_stack([xs + ox, ys + oy]).astype(np.float32)
+        ink_pixels = int(len(points))
+
+        x1, y1 = float(points[:, 0].min()), float(points[:, 1].min())
+        x2, y2 = float(points[:, 0].max()) + 1.0, float(points[:, 1].max()) + 1.0
+        bbox: Box = (x1, y1, x2, y2)
+
+        rect = cv2.minAreaRect(points)
+        (rcx, rcy), (rw, rh), rangle = rect
+        rotated_rect = {
+            "center": [round(float(rcx), 2), round(float(rcy), 2)],
+            "size": [round(float(rw), 2), round(float(rh), 2)],
+            "angle_deg": round(float(rangle), 2),
+        }
+
+        # Convex hull of every detected ink pixel, simplified with
+        # approxPolyDP (epsilon = 1% of hull perimeter) -- matches
+        # projectA's own signature_polygon_px computation exactly
+        # (geometry_metrics.py::compute_signature_placement_metrics()),
+        # so this pipeline's polygon behaves the same way projectA's does.
+        contour_points = points.astype(np.int32)
+        hull = cv2.convexHull(contour_points)
+        epsilon = 0.01 * cv2.arcLength(hull, True)
+        approx = cv2.approxPolyDP(hull, epsilon, True)
+        hull_xy = [[int(pt[0][0]), int(pt[0][1])] for pt in approx]
+
+        # Real per-blob boundary contour(s) -- for the visual preview only
+        # (see the display_contours field docstring on GeometryResult).
+        # A small dilation first (on a COPY, never the scoring mask) merges
+        # adjacent pen-stroke fragments of the same letter into one smooth
+        # blob instead of a "polka dot" scatter of tiny separate islands --
+        # a few px is nowhere near enough to bridge a real gap between
+        # separate letters or a flourish far from the main strokes, so
+        # genuine whitespace still stays outside every contour.
+        dilated = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=1)
+        raw_contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        display_contours = [
+            [[float(pt[0][0] + ox), float(pt[0][1] + oy)] for pt in c]
+            for c in raw_contours if len(c) >= 3
+        ]
+
+        mean_pt, eigvecs, eigvals = cv2.PCACompute2(points, mean=None)
+        orientation_deg = float(np.degrees(np.arctan2(eigvecs[0][1], eigvecs[0][0])))
+        orientation_deg = ((orientation_deg + 90) % 180) - 90
+
+        centroid = (float(points[:, 0].mean()), float(points[:, 1].mean()))
+
+        bbox_crop = full_gray[int(y1):int(y2), int(x1):int(x2)]
+        mean_dark_intensity = float(bbox_crop[mask.astype(bool)].mean()) if bbox_crop.shape == mask.shape else float(
+            full_gray[oy:oy + mask.shape[0], ox:ox + mask.shape[1]][mask.astype(bool)].mean()
+        )
+        gray_stddev = float(bbox_crop.std()) if bbox_crop.size > 0 else 0.0
+
+        target_poly = box_to_polygon(target_box)
+        signature_poly = points_to_polygon(hull_xy)
+
+        bbox_area = max((x2 - x1) * (y2 - y1), 1.0)
+        ink_fill_ratio = float(np.clip(ink_pixels / bbox_area, 0.0, 1.0))
+        canvas_area = max(canvas_size[0] * canvas_size[1], 1)
+        signature_area_ratio = float(bbox_area / canvas_area)
+
+        return GeometryResult(
+            found=True,
+            signature_bbox_px=bbox,
+            min_rotated_rect=rotated_rect,
+            signature_polygon_px=hull_xy,
+            convex_hull_px=hull_xy,
+            display_contours=display_contours,
+            centroid_px=(round(centroid[0], 2), round(centroid[1], 2)),
+            orientation_deg=round(orientation_deg, 2),
+            ink_pixels=ink_pixels,
+            target_box=target_box,
+            actual_box=bbox,
+            search_box=search_box,
+            box_coverage_pct=round(coverage_pct(target_poly, signature_poly), 3),
+            containment_pct=round(containment_pct(target_poly, signature_poly), 3),
+            ink_fill_ratio=round(ink_fill_ratio, 4),
+            polygon_overlap_proxy=round(iou(target_poly, signature_poly), 4),
+            distance_from_box_center_px=round(distance_between(box_center(target_box), centroid), 2),
+            margin_utilization=round(margin_utilization(target_box, signature_poly), 4),
+            mean_dark_intensity=round(mean_dark_intensity, 2),
+            gray_stddev=round(gray_stddev, 2),
+            signature_area_ratio=round(signature_area_ratio, 5),
+            whitespace_ratio=round(float(np.clip(1.0 - ink_fill_ratio, 0.0, 1.0)), 4),
+        )
+
+    @staticmethod
+    def _empty_result(target_box: Box, search_box: Box, canvas_size: tuple[int, int]) -> GeometryResult:
+        return GeometryResult(
+            found=False,
+            signature_bbox_px=target_box,
+            min_rotated_rect={"center": [0.0, 0.0], "size": [0.0, 0.0], "angle_deg": 0.0},
+            signature_polygon_px=[],
+            convex_hull_px=[],
+            display_contours=[],
+            centroid_px=(float("nan"), float("nan")),
+            orientation_deg=0.0,
+            ink_pixels=0,
+            target_box=target_box,
+            actual_box=target_box,
+            search_box=search_box,
+            box_coverage_pct=0.0,
+            containment_pct=0.0,
+            ink_fill_ratio=0.0,
+            polygon_overlap_proxy=0.0,
+            distance_from_box_center_px=float("nan"),
+            margin_utilization=0.0,
+            mean_dark_intensity=255.0,
+            gray_stddev=0.0,
+            signature_area_ratio=0.0,
+            whitespace_ratio=1.0,
+        )
diff --git a/unified_pipeline/src/analysis/validation_engine.py b/unified_pipeline/src/analysis/validation_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..f42e8431699e1ad8a85d1103be4e15f6c1085bc8
--- /dev/null
+++ b/unified_pipeline/src/analysis/validation_engine.py
@@ -0,0 +1,282 @@
+"""Post-generation validation of exported metadata.
+
+Checks that every sample's metadata is internally consistent and safe for
+downstream ML consumption: the signature was actually detected, its polygon
+is a valid non-degenerate geometry, every declared bounding box is
+well-formed and lies on the page, no NaN/Inf values leaked into the JSON,
+metadata is schema-complete, and the difficulty score is exactly
+reproducible from its own recorded components and weights.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+from typing import Any
+
+from shapely.geometry import Polygon
+
+from src.config.schema import DifficultyWeights, ValidationConfig
+from src.utils.logging_utils import get_logger
+from src.verification.case_types import CASE_TABLE
+
+logger = get_logger(__name__)
+
+REQUIRED_TOP_LEVEL_KEYS = (
+    "document_id", "template", "page", "page_size", "signer", "signature_source",
+    "placement", "rendering", "difficulty", "quality", "verification", "augmentation", "geometry", "metrics",
+)
+
+# Case types whose ground truth is an intentionally blank target box -- a
+# real signature is not expected to be detected there.
+_BLANK_CASE_TYPES = frozenset({"2a", "5a"})
+
+
+@dataclass
+class ValidationIssue:
+    field: str
+    message: str
+    severity: str  # "error" | "warning"
+
+
+@dataclass
+class ValidationReport:
+    sample_id: str
+    passed: bool
+    issues: list[ValidationIssue] = field(default_factory=list)
+
+    @property
+    def errors(self) -> list[ValidationIssue]:
+        return [i for i in self.issues if i.severity == "error"]
+
+    @property
+    def warnings(self) -> list[ValidationIssue]:
+        return [i for i in self.issues if i.severity == "warning"]
+
+
+class ValidationEngine:
+    """Validates one sample's exported metadata for correctness and completeness.
+
+    Example:
+        >>> engine = ValidationEngine(cfg.validation, cfg.difficulty)
+        >>> report = engine.validate(metadata)
+        >>> assert report.passed
+    """
+
+    def __init__(self, config: ValidationConfig, difficulty_weights: DifficultyWeights) -> None:
+        self.config = config
+        self.weights = difficulty_weights
+
+    def validate(self, metadata: dict[str, Any]) -> ValidationReport:
+        """Run every validation check against one sample's metadata dict."""
+        sample_id = str(metadata.get("document_id", "unknown"))
+        issues: list[ValidationIssue] = []
+
+        issues += self._check_completeness(metadata)
+        issues += self._check_no_nan(metadata)
+        issues += self._check_signature_exists(metadata)
+        issues += self._check_polygon_valid(metadata)
+        issues += self._check_boxes(metadata)
+        issues += self._check_difficulty_reproducible(metadata)
+        issues += self._check_verification_consistency(metadata)
+
+        has_errors = any(i.severity == "error" for i in issues)
+        has_warnings = any(i.severity == "warning" for i in issues)
+        passed = not has_errors and not (self.config.strict and has_warnings)
+
+        return ValidationReport(sample_id=sample_id, passed=passed, issues=issues)
+
+    def _check_completeness(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
+        issues = []
+        for key in REQUIRED_TOP_LEVEL_KEYS:
+            if key not in metadata:
+                issues.append(ValidationIssue(key, f"Missing required top-level key '{key}'", "error"))
+        signer = metadata.get("signer", {})
+        for key in ("name", "role"):
+            if not signer.get(key):
+                issues.append(ValidationIssue(f"signer.{key}", f"Missing signer.{key}", "error"))
+        return issues
+
+    def _check_no_nan(self, metadata: dict[str, Any], path: str = "") -> list[ValidationIssue]:
+        issues: list[ValidationIssue] = []
+
+        # geometry.centroid_px / metrics.distance_from_box_center_px are
+        # deliberate NaN sentinels (see GeometryAnalyzer._empty_result) when
+        # nothing was detected -- not a data-quality bug, so they're
+        # excluded from the walk. Note distance_from_box_center_px is
+        # exported under "metrics", not "geometry" (see
+        # MetadataExporter.build) -- this previously said "geometry.distance_..."
+        # and so never actually matched anything, silently failing to skip
+        # it; invisible until a genuinely blank-target case (2a/5a) was
+        # ever correctly detected as such, since contamination in ink
+        # detection had been making even deliberately-blank boxes report
+        # SOME (spurious) ink found beforehand.
+        skip_paths: set = set()
+        if not metadata.get("geometry", {}).get("detected", True):
+            skip_paths = {"geometry.centroid_px[0]", "geometry.centroid_px[1]", "metrics.distance_from_box_center_px"}
+
+        def walk(value: Any, cur_path: str) -> None:
+            if cur_path in skip_paths:
+                return
+            if isinstance(value, dict):
+                for k, v in value.items():
+                    walk(v, f"{cur_path}.{k}" if cur_path else str(k))
+            elif isinstance(value, (list, tuple)):
+                for i, v in enumerate(value):
+                    walk(v, f"{cur_path}[{i}]")
+            elif isinstance(value, float):
+                if math.isnan(value) or math.isinf(value):
+                    issues.append(ValidationIssue(cur_path, f"Non-finite value ({value}) at {cur_path}", "error"))
+
+        walk(metadata, path)
+        return issues
+
+    @staticmethod
+    def _expects_blank_target(metadata: dict[str, Any]) -> bool:
+        verification = metadata.get("verification") or {}
+        return verification.get("case_type") in _BLANK_CASE_TYPES
+
+    def _check_signature_exists(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
+        geometry = metadata.get("geometry", {})
+        if self._expects_blank_target(metadata):
+            return []
+        if not geometry.get("detected", False):
+            return [ValidationIssue("geometry.detected", "Signature was not detected in the rendered document", "error")]
+        if geometry.get("ink_pixels", 0) <= 0:
+            return [ValidationIssue("geometry.ink_pixels", "Signature detected but has zero ink pixels", "error")]
+        return []
+
+    def _check_polygon_valid(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
+        issues: list[ValidationIssue] = []
+        if not metadata.get("geometry", {}).get("detected", False) and self._expects_blank_target(metadata):
+            return []
+        poly_pts = metadata.get("geometry", {}).get("signature_polygon_px", [])
+        if len(poly_pts) < 3:
+            issues.append(ValidationIssue("geometry.signature_polygon_px", "Polygon has fewer than 3 points", "error"))
+            return issues
+        try:
+            poly = Polygon(poly_pts)
+            if not poly.is_valid:
+                issues.append(ValidationIssue("geometry.signature_polygon_px", "Polygon geometry is invalid", "error"))
+            elif poly.area <= 0:
+                issues.append(ValidationIssue("geometry.signature_polygon_px", "Polygon has zero area", "error"))
+        except Exception as exc:  # pragma: no cover - defensive
+            issues.append(ValidationIssue("geometry.signature_polygon_px", f"Polygon construction failed: {exc}", "error"))
+        return issues
+
+    def _check_boxes(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
+        issues: list[ValidationIssue] = []
+        geometry = metadata.get("geometry", {})
+        page_w, page_h = metadata.get("page_size", [None, None])
+        eps = self.config.max_coordinate_epsilon
+
+        for field_name in ("target_box", "actual_box", "signature_bbox_px", "box_px"):
+            box = geometry.get(field_name)
+            if not box or len(box) != 4:
+                issues.append(ValidationIssue(f"geometry.{field_name}", "Box missing or malformed", "error"))
+                continue
+            x1, y1, x2, y2 = box
+            if x2 <= x1 or y2 <= y1:
+                issues.append(ValidationIssue(f"geometry.{field_name}", f"Box has non-positive extent: {box}", "error"))
+            if page_w is not None and page_h is not None:
+                if x1 < -eps or y1 < -eps or x2 > page_w + eps or y2 > page_h + eps:
+                    issues.append(ValidationIssue(
+                        f"geometry.{field_name}",
+                        f"Box {box} lies outside page bounds (0,0,{page_w},{page_h})",
+                        "warning",
+                    ))
+        return issues
+
+    def _check_difficulty_reproducible(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
+        difficulty = metadata.get("difficulty", {})
+        required = ("misplacement", "whitespace", "faintness", "smallness", "low_texture", "score", "tier")
+        if not all(k in difficulty for k in required):
+            return [ValidationIssue("difficulty", "Difficulty block incomplete", "error")]
+
+        w = self.weights
+        recomputed = (
+            w.misplacement * difficulty["misplacement"]
+            + w.whitespace * difficulty["whitespace"]
+            + w.faintness * difficulty["faintness"]
+            + w.smallness * difficulty["smallness"]
+            + w.low_texture * difficulty["low_texture"]
+        )
+        if abs(recomputed - difficulty["score"]) > 1e-3:
+            return [ValidationIssue(
+                "difficulty.score",
+                f"Score {difficulty['score']} does not match recomputed weighted sum {recomputed:.4f}",
+                "error",
+            )]
+
+        if recomputed < w.easy_threshold:
+            expected_tier = "easy"
+        elif recomputed < w.hard_threshold:
+            expected_tier = "medium"
+        else:
+            expected_tier = "hard"
+        if expected_tier != difficulty["tier"]:
+            return [ValidationIssue(
+                "difficulty.tier", f"Tier '{difficulty['tier']}' does not match expected '{expected_tier}'", "error",
+            )]
+        return []
+
+    def _check_verification_consistency(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
+        verification = metadata.get("verification") or {}
+        case_type = verification.get("case_type")
+        if case_type is None:
+            return []
+
+        spec = CASE_TABLE.get(case_type)
+        if spec is None:
+            return [ValidationIssue("verification.case_type", f"Unknown case_type '{case_type}'", "error")]
+
+        issues: list[ValidationIssue] = []
+        if verification.get("verdict") != spec.verdict:
+            issues.append(ValidationIssue(
+                "verification.verdict",
+                f"verdict '{verification.get('verdict')}' does not match case_type '{case_type}' expected '{spec.verdict}'",
+                "error",
+            ))
+        if verification.get("reject_code") != spec.reject_code:
+            issues.append(ValidationIssue(
+                "verification.reject_code",
+                f"reject_code '{verification.get('reject_code')}' does not match case_type '{case_type}' expected '{spec.reject_code}'",
+                "error",
+            ))
+        return issues
+
+
+@dataclass
+class ValidationSummary:
+    """Aggregate statistics over a batch of per-sample validation reports."""
+
+    total: int
+    passed: int
+    failed: int
+    total_errors: int
+    total_warnings: int
+    failed_sample_ids: list[str]
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "total": self.total,
+            "passed": self.passed,
+            "failed": self.failed,
+            "pass_rate": round(self.passed / self.total, 4) if self.total else 0.0,
+            "total_errors": self.total_errors,
+            "total_warnings": self.total_warnings,
+            "failed_sample_ids": self.failed_sample_ids,
+        }
+
+
+def summarize(reports: list[ValidationReport]) -> ValidationSummary:
+    """Aggregate a list of per-sample validation reports into a batch summary."""
+    passed = sum(1 for r in reports if r.passed)
+    return ValidationSummary(
+        total=len(reports),
+        passed=passed,
+        failed=len(reports) - passed,
+        total_errors=sum(len(r.errors) for r in reports),
+        total_warnings=sum(len(r.warnings) for r in reports),
+        failed_sample_ids=[r.sample_id for r in reports if not r.passed],
+    )
diff --git a/unified_pipeline/src/augmentation/__init__.py b/unified_pipeline/src/augmentation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4b5871f81f066d6496a9f7288a56653167fe601
--- /dev/null
+++ b/unified_pipeline/src/augmentation/__init__.py
@@ -0,0 +1 @@
+"""Configurable augmentation effects for signature layers and full documents."""
diff --git a/unified_pipeline/src/augmentation/albumentations_effects.py b/unified_pipeline/src/augmentation/albumentations_effects.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b80bff7650d13017fa8c50cdc90e487814262a1
--- /dev/null
+++ b/unified_pipeline/src/augmentation/albumentations_effects.py
@@ -0,0 +1,76 @@
+"""Thin, deterministic wrappers around Albumentations transforms.
+
+Every wrapper collapses the transform's own randomized magnitude range down
+to values derived from our ``strength in [0, 1]`` convention, and executes
+via a freshly seeded ``A.Compose`` so results are fully reproducible under
+:mod:`src.utils.random_utils` seed derivation rather than Albumentations'
+global random state.
+"""
+
+from __future__ import annotations
+
+import albumentations as A
+import numpy as np
+
+
+def _run(transform: "A.BasicTransform", image: np.ndarray, seed: int) -> np.ndarray:
+    pipeline = A.Compose([transform], seed=seed, p=1.0)
+    return pipeline(image=image)["image"]
+
+
+def motion_blur(image: np.ndarray, strength: float, seed: int) -> np.ndarray:
+    """Directional motion blur; ``strength`` controls kernel size (3..21 px)."""
+    k = int(round(3 + strength * 18))
+    k = k + 1 if k % 2 == 0 else k
+    k = max(3, min(k, 21))
+    return _run(A.MotionBlur(blur_limit=(k, k), p=1.0), image, seed)
+
+
+def gaussian_blur(image: np.ndarray, strength: float, seed: int) -> tuple[np.ndarray, float]:
+    """Isotropic Gaussian blur; ``strength`` controls sigma (0.2..4.0 px)."""
+    sigma = round(0.2 + strength * 3.8, 3)
+    out = _run(A.GaussianBlur(blur_limit=0, sigma_limit=(sigma, sigma), p=1.0), image, seed)
+    return out, sigma
+
+
+def perspective_warp(image: np.ndarray, strength: float, seed: int) -> np.ndarray:
+    """Subtle perspective distortion; ``strength`` controls corner-jitter scale."""
+    scale = round(0.01 + strength * 0.09, 4)
+    return _run(A.Perspective(scale=(scale, scale), keep_size=True, fit_output=False, p=1.0), image, seed)
+
+
+def contrast_shift(image: np.ndarray, strength: float, seed: int) -> np.ndarray:
+    """Global contrast shift; ``strength`` controls the max +/- magnitude."""
+    mag = round(strength * 0.45, 4)
+    t = A.RandomBrightnessContrast(brightness_limit=(0.0, 0.0), contrast_limit=(-mag, mag), p=1.0)
+    return _run(t, image, seed)
+
+
+def gamma_shift(image: np.ndarray, strength: float, seed: int) -> tuple[np.ndarray, int]:
+    """Gamma correction; ``strength`` controls deviation from gamma=100."""
+    delta = int(round(strength * 45))
+    lo, hi = max(100 - delta, 40), 100 + delta
+    out = _run(A.RandomGamma(gamma_limit=(lo, hi), p=1.0), image, seed)
+    return out, lo if lo != hi else lo
+
+
+def jpeg_compression(image: np.ndarray, min_quality: int, max_quality: int, seed: int) -> tuple[np.ndarray, int]:
+    """JPEG re-compression artifact simulation over ``[min_quality, max_quality]``."""
+    rng = np.random.default_rng(seed)
+    quality = int(rng.integers(min_quality, max_quality + 1))
+    out = _run(A.ImageCompression(compression_type="jpeg", quality_range=(quality, quality), p=1.0), image, seed)
+    return out, quality
+
+
+def salt_and_pepper(image: np.ndarray, strength: float, seed: int) -> np.ndarray:
+    """Salt-and-pepper impulse noise; ``strength`` controls the affected pixel fraction."""
+    amount = round(0.002 + strength * 0.09, 5)
+    t = A.SaltAndPepper(amount=(amount, amount), salt_vs_pepper=(0.45, 0.55), p=1.0)
+    return _run(t, image, seed)
+
+
+def sensor_noise(image: np.ndarray, strength: float, seed: int) -> np.ndarray:
+    """Gaussian sensor/scanner noise; ``strength`` controls the noise std as a fraction of range."""
+    std = round(0.01 + strength * 0.12, 5)
+    t = A.GaussNoise(std_range=(std, std), mean_range=(0.0, 0.0), per_channel=False, p=1.0)
+    return _run(t, image, seed)
diff --git a/unified_pipeline/src/augmentation/augmentation_engine.py b/unified_pipeline/src/augmentation/augmentation_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..e418308ffe78c0e2a47070fcdb77b0f592e629b4
--- /dev/null
+++ b/unified_pipeline/src/augmentation/augmentation_engine.py
@@ -0,0 +1,290 @@
+"""Orchestrates every configurable augmentation effect for one sample.
+
+Two passes are performed:
+
+1. :meth:`AugmentationEngine.apply_to_signature` -- effects that only make
+   sense applied to the ink itself (stroke geometry, ink density, localized
+   blur/jitter), run on the RGBA signature layer *before* blending.
+2. :meth:`AugmentationEngine.apply_to_document` -- effects that simulate
+   physical document/scanner degradation, run on the fully blended page.
+
+Every effect is gated independently by its own ``enabled`` flag, a
+probability roll, and a strength value -- each resolved from a seed derived
+deterministically from ``(sample_seed, augmentation_name)`` (or an explicit
+per-effect ``seed`` override in config), guaranteeing reproducibility.
+Every effect's resolved parameters are collected into an
+:class:`AugmentationRecord` for full metadata traceability, even when the
+effect was not applied.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+
+from src.augmentation import albumentations_effects as AE
+from src.augmentation import document_effects as DE
+from src.augmentation import signature_effects as SE
+from src.config.schema import AugmentationParam, AugmentationsConfig
+from src.utils.logging_utils import get_logger
+from src.utils.random_utils import SeedManager, resolve_scalar_or_range
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class AugmentationRecord:
+    """The resolved, applied-or-not state of a single augmentation effect."""
+
+    name: str
+    enabled: bool
+    applied: bool
+    strength: float
+    probability: float
+    seed: int
+    params: dict[str, Any] = field(default_factory=dict)
+
+
+def _prepare(name: str, cfg: AugmentationParam, seed_manager: SeedManager) -> tuple[bool, np.random.Generator, int]:
+    seed = cfg.seed if cfg.seed is not None else seed_manager.seed_for(f"augmentation:{name}")
+    rng = np.random.default_rng(seed)
+    roll = float(rng.uniform(0.0, 1.0))
+    applied = bool(cfg.enabled and roll < cfg.probability)
+    return applied, rng, seed
+
+
+def _record(name: str, cfg: AugmentationParam, applied: bool, seed: int,
+            strength: float = 0.0, params: "dict | None" = None) -> AugmentationRecord:
+    return AugmentationRecord(
+        name=name, enabled=cfg.enabled, applied=applied, strength=round(strength, 4),
+        probability=cfg.probability, seed=seed, params=params or {},
+    )
+
+
+class AugmentationEngine:
+    """Applies the full configurable augmentation suite to a signature layer and a document.
+
+    Example:
+        >>> engine = AugmentationEngine(cfg.augmentation)
+        >>> rgba, sig_records = engine.apply_to_signature(placed_rgba, seed_manager)
+        >>> doc, doc_records, warped_baseline = engine.apply_to_document(blended_doc, seed_manager, baseline_image)
+    """
+
+    def __init__(self, config: AugmentationsConfig) -> None:
+        self.config = config
+
+    # ------------------------------------------------------------------ #
+    # Signature-layer effects
+    # ------------------------------------------------------------------ #
+    def apply_to_signature(
+        self, rgba: np.ndarray, seed_manager: SeedManager,
+    ) -> tuple[np.ndarray, dict[str, AugmentationRecord]]:
+        cfg = self.config
+        records: dict[str, AugmentationRecord] = {}
+        out = rgba
+
+        applied, rng, seed = _prepare("rotation", cfg.rotation, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.rotation.strength, rng)
+            extra_deg = float(rng.uniform(-1, 1)) * strength * 8.0
+            out = SE.jitter_rotation(out, extra_deg)
+            records["rotation"] = _record("rotation", cfg.rotation, True, seed, strength, {"extra_deg": round(extra_deg, 3)})
+        else:
+            records["rotation"] = _record("rotation", cfg.rotation, False, seed)
+
+        applied, rng, seed = _prepare("stroke_dilation", cfg.stroke_dilation, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.stroke_dilation.strength, rng)
+            out = SE.stroke_morphology(out, strength, thicken=True)
+            records["stroke_dilation"] = _record("stroke_dilation", cfg.stroke_dilation, True, seed, strength, {"thicken": True})
+        else:
+            records["stroke_dilation"] = _record("stroke_dilation", cfg.stroke_dilation, False, seed)
+
+        applied, rng, seed = _prepare("ink_fading", cfg.ink_fading, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.ink_fading.strength, rng)
+            out = SE.ink_fading(out, strength, rng)
+            records["ink_fading"] = _record("ink_fading", cfg.ink_fading, True, seed, strength)
+        else:
+            records["ink_fading"] = _record("ink_fading", cfg.ink_fading, False, seed)
+
+        applied, rng, seed = _prepare("ink_bleed", cfg.ink_bleed, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.ink_bleed.strength, rng)
+            out = SE.ink_bleed(out, strength)
+            records["ink_bleed"] = _record("ink_bleed", cfg.ink_bleed, True, seed, strength)
+        else:
+            records["ink_bleed"] = _record("ink_bleed", cfg.ink_bleed, False, seed)
+
+        applied, rng, seed = _prepare("motion_blur", cfg.motion_blur, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.motion_blur.strength, rng)
+            angle = float(rng.uniform(0, 180))
+            out = SE.motion_blur_signature(out, strength, angle)
+            records["motion_blur"] = _record("motion_blur", cfg.motion_blur, True, seed, strength, {"angle_deg": round(angle, 2)})
+        else:
+            records["motion_blur"] = _record("motion_blur", cfg.motion_blur, False, seed)
+
+        applied, rng, seed = _prepare("gaussian_blur", cfg.gaussian_blur, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.gaussian_blur.strength, rng)
+            sigma = 0.2 + strength * 2.2
+            out = SE.gaussian_blur_signature(out, sigma)
+            records["gaussian_blur"] = _record("gaussian_blur", cfg.gaussian_blur, True, seed, strength, {"sigma": round(sigma, 3)})
+        else:
+            records["gaussian_blur"] = _record("gaussian_blur", cfg.gaussian_blur, False, seed)
+
+        return out, records
+
+    # ------------------------------------------------------------------ #
+    # Document-level effects
+    # ------------------------------------------------------------------ #
+    def apply_to_document(
+        self, image: np.ndarray, seed_manager: SeedManager, baseline_image: "np.ndarray | None" = None,
+    ) -> tuple[np.ndarray, dict[str, AugmentationRecord], "np.ndarray | None"]:
+        """Runs the full document-level effect suite.
+
+        Args:
+            image: The fully-signed document to degrade.
+            seed_manager: Derives each effect's seed deterministically.
+            baseline_image: Optional blank/unsigned template, same canvas
+                size as ``image`` -- when given, the 3 geometric effects
+                (perspective/crumple/page_tilt) apply the exact same
+                transform to it too, so the returned ``warped_baseline`` (3rd
+                tuple element) stays pixel-aligned with ``image`` even after
+                a rotation/warp, and callers can keep using baseline-based
+                ink-exclusion instead of having to skip it. ``None`` in,
+                ``None`` out.
+
+        Returns:
+            ``(degraded_image, per_effect_records, warped_baseline)``.
+        """
+        cfg = self.config
+        records: dict[str, AugmentationRecord] = {}
+        out = image
+        baseline_out = baseline_image
+
+        applied, rng, seed = _prepare("perspective", cfg.perspective, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.perspective.strength, rng)
+            out = AE.perspective_warp(out, strength, seed)
+            if baseline_out is not None:
+                # Deterministic from (strength, seed) alone (Albumentations
+                # reseeds its own RNG from `seed` each call) -- calling it
+                # again on the baseline reproduces the identical warp.
+                baseline_out = AE.perspective_warp(baseline_out, strength, seed)
+            records["perspective"] = _record("perspective", cfg.perspective, True, seed, strength)
+        else:
+            records["perspective"] = _record("perspective", cfg.perspective, False, seed)
+
+        applied, rng, seed = _prepare("contrast", cfg.contrast, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.contrast.strength, rng)
+            out = AE.contrast_shift(out, strength, seed)
+            records["contrast"] = _record("contrast", cfg.contrast, True, seed, strength)
+        else:
+            records["contrast"] = _record("contrast", cfg.contrast, False, seed)
+
+        applied, rng, seed = _prepare("gamma", cfg.gamma, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.gamma.strength, rng)
+            out, gamma_value = AE.gamma_shift(out, strength, seed)
+            records["gamma"] = _record("gamma", cfg.gamma, True, seed, strength, {"gamma": gamma_value})
+        else:
+            records["gamma"] = _record("gamma", cfg.gamma, False, seed)
+
+        applied, rng, seed = _prepare("coffee_stain", cfg.coffee_stain, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.coffee_stain.strength, rng)
+            out, params = DE.coffee_stain(out, strength, rng)
+            records["coffee_stain"] = _record("coffee_stain", cfg.coffee_stain, True, seed, strength, params)
+        else:
+            records["coffee_stain"] = _record("coffee_stain", cfg.coffee_stain, False, seed)
+
+        applied, rng, seed = _prepare("stain", cfg.stain, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.stain.strength, rng)
+            out, params = DE.stain(out, strength, cfg.stain.type, rng)
+            records["stain"] = _record("stain", cfg.stain, True, seed, strength, params)
+        else:
+            records["stain"] = _record("stain", cfg.stain, False, seed)
+
+        applied, rng, seed = _prepare("shadow", cfg.shadow, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.shadow.strength, rng)
+            out, params = DE.shadow(out, strength, cfg.shadow.type, rng)
+            records["shadow"] = _record("shadow", cfg.shadow, True, seed, strength, params)
+        else:
+            records["shadow"] = _record("shadow", cfg.shadow, False, seed)
+
+        applied, rng, seed = _prepare("crumple", cfg.crumple, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.crumple.strength, rng)
+            density = resolve_scalar_or_range(cfg.crumple.wrinkle_density, rng)
+            out, params, baseline_out = DE.crumple(out, strength, density, rng, companion=baseline_out)
+            records["crumple"] = _record("crumple", cfg.crumple, True, seed, strength, {**params, "wrinkle_density": round(density, 3)})
+        else:
+            records["crumple"] = _record("crumple", cfg.crumple, False, seed)
+
+        applied, rng, seed = _prepare("discoloration", cfg.discoloration, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.discoloration.strength, rng)
+            out, params = DE.discoloration(out, strength, cfg.discoloration.tone, rng)
+            records["discoloration"] = _record("discoloration", cfg.discoloration, True, seed, strength, params)
+        else:
+            records["discoloration"] = _record("discoloration", cfg.discoloration, False, seed)
+
+        applied, rng, seed = _prepare("page_tilt", cfg.page_tilt, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.page_tilt.strength, rng)
+            out, params, baseline_out = DE.page_tilt(out, strength, rng, companion=baseline_out)
+            records["page_tilt"] = _record("page_tilt", cfg.page_tilt, True, seed, strength, params)
+        else:
+            records["page_tilt"] = _record("page_tilt", cfg.page_tilt, False, seed)
+
+        applied, rng, seed = _prepare("dust", cfg.dust, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.dust.strength, rng)
+            out, params = DE.dust(out, strength, rng)
+            records["dust"] = _record("dust", cfg.dust, True, seed, strength, params)
+        else:
+            records["dust"] = _record("dust", cfg.dust, False, seed)
+
+        applied, rng, seed = _prepare("scanner_streaks", cfg.scanner_streaks, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.scanner_streaks.strength, rng)
+            out, params = DE.scanner_streaks(out, strength, rng)
+            records["scanner_streaks"] = _record("scanner_streaks", cfg.scanner_streaks, True, seed, strength, params)
+        else:
+            records["scanner_streaks"] = _record("scanner_streaks", cfg.scanner_streaks, False, seed)
+
+        applied, rng, seed = _prepare("scanner_noise", cfg.scanner_noise, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.scanner_noise.strength, rng)
+            out = AE.sensor_noise(out, strength, seed)
+            records["scanner_noise"] = _record("scanner_noise", cfg.scanner_noise, True, seed, strength)
+        else:
+            records["scanner_noise"] = _record("scanner_noise", cfg.scanner_noise, False, seed)
+
+        applied, rng, seed = _prepare("salt_pepper", cfg.salt_pepper, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(cfg.salt_pepper.strength, rng)
+            out = AE.salt_and_pepper(out, strength, seed)
+            records["salt_pepper"] = _record("salt_pepper", cfg.salt_pepper, True, seed, strength)
+        else:
+            records["salt_pepper"] = _record("salt_pepper", cfg.salt_pepper, False, seed)
+
+        # JPEG compression must run last: it is a lossy re-encode and every
+        # subsequent pixel-level effect would otherwise mask its artifacts.
+        jpeg_cfg = cfg.jpeg_compression
+        applied, rng, seed = _prepare("jpeg_compression", jpeg_cfg, seed_manager)
+        if applied:
+            strength = resolve_scalar_or_range(jpeg_cfg.strength, rng)
+            out, quality = AE.jpeg_compression(out, jpeg_cfg.min_quality, jpeg_cfg.max_quality, seed)
+            records["jpeg_compression"] = _record("jpeg_compression", jpeg_cfg, True, seed, strength, {"quality": quality})
+        else:
+            records["jpeg_compression"] = _record("jpeg_compression", jpeg_cfg, False, seed)
+
+        return out, records, baseline_out
diff --git a/unified_pipeline/src/augmentation/document_effects.py b/unified_pipeline/src/augmentation/document_effects.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab3a44c1c2a0386d90dc90bfff01ad44c451e4b3
--- /dev/null
+++ b/unified_pipeline/src/augmentation/document_effects.py
@@ -0,0 +1,267 @@
+"""Custom augmentation effects operating on the full, already-blended document image.
+
+These simulate physical document degradation and scanning artifacts that
+Albumentations has no domain-specific transform for: coffee/water/paper
+stains, directional shadows, page creasing, discoloration, dust, and
+scanner streaks. Each function returns ``(image, actual_params)`` so the
+exact resolved parameters (e.g. which corner a shadow fell in) can be
+recorded in metadata.
+"""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from src.utils.transform_utils import smooth_noise_field
+
+
+def _blob_mask(shape: tuple[int, int], center: tuple[float, float], radii: tuple[float, float],
+                angle_deg: float, blur_sigma: float) -> np.ndarray:
+    """Build a soft-edged elliptical blob mask in ``[0, 1]``."""
+    h, w = shape
+    mask = np.zeros((h, w), dtype=np.uint8)
+    cv2.ellipse(mask, (int(center[0]), int(center[1])), (int(radii[0]), int(radii[1])), angle_deg, 0, 360, 255, -1)
+    mask_f = cv2.GaussianBlur(mask.astype(np.float32), (0, 0), sigmaX=blur_sigma) / 255.0
+    return np.clip(mask_f, 0, 1)
+
+
+def coffee_stain(image: np.ndarray, strength: float, rng: np.random.Generator) -> tuple[np.ndarray, dict]:
+    """Overlay 1-3 translucent brown ring-edged blotches, like a coffee cup mark."""
+    h, w = image.shape[:2]
+    out = image.astype(np.float32)
+    n_blobs = 1 + int(rng.integers(0, 1 + int(round(strength * 3))))
+    centers = []
+    for _ in range(max(n_blobs, 1)):
+        cx, cy = rng.uniform(0.1, 0.9) * w, rng.uniform(0.1, 0.9) * h
+        base_r = (0.03 + strength * 0.10) * min(w, h)
+        rx, ry = base_r * rng.uniform(0.8, 1.3), base_r * rng.uniform(0.7, 1.2)
+        angle = float(rng.uniform(0, 180))
+        fill = _blob_mask((h, w), (cx, cy), (rx, ry), angle, blur_sigma=base_r * 0.35)
+        rim = fill - _blob_mask((h, w), (cx, cy), (rx * 0.82, ry * 0.82), angle, blur_sigma=base_r * 0.2)
+        color = np.array([139, 98, 60], dtype=np.float32)
+        opacity = 0.10 + strength * 0.30
+        for c in range(3):
+            out[:, :, c] = out[:, :, c] * (1 - fill * opacity * 0.5) + color[c] * fill * opacity * 0.5
+            out[:, :, c] = out[:, :, c] * (1 - rim * opacity * 0.9) + color[c] * 0.7 * rim * opacity * 0.9
+        centers.append((round(float(cx), 1), round(float(cy), 1)))
+    return np.clip(out, 0, 255).astype(np.uint8), {"blob_count": len(centers), "centers": centers}
+
+
+def stain(image: np.ndarray, strength: float, stain_type: str, rng: np.random.Generator) -> tuple[np.ndarray, dict]:
+    """Overlay a paper or water stain blotch.
+
+    ``paper`` stains are mottled brownish-grey discolorations; ``water``
+    stains are pale, soft-edged, faintly blue-grey tide marks.
+    """
+    h, w = image.shape[:2]
+    out = image.astype(np.float32)
+    resolved_type = stain_type if stain_type != "random" else rng.choice(["paper", "water"])
+    cx, cy = rng.uniform(0.15, 0.85) * w, rng.uniform(0.15, 0.85) * h
+    base_r = (0.06 + strength * 0.16) * min(w, h)
+
+    if resolved_type == "water":
+        color = np.array([170, 178, 182], dtype=np.float32)
+        opacity = 0.06 + strength * 0.18
+        rx, ry = base_r * rng.uniform(1.0, 1.6), base_r * rng.uniform(0.8, 1.3)
+        fill = _blob_mask((h, w), (cx, cy), (rx, ry), float(rng.uniform(0, 180)), blur_sigma=base_r * 0.6)
+        edge = fill - _blob_mask((h, w), (cx, cy), (rx * 0.9, ry * 0.9), 0, blur_sigma=base_r * 0.3)
+        for c in range(3):
+            out[:, :, c] = out[:, :, c] * (1 - fill * opacity * 0.4) + color[c] * fill * opacity * 0.4
+            out[:, :, c] = out[:, :, c] * (1 - edge * opacity) + color[c] * edge * opacity
+    else:
+        color = np.array([120, 106, 84], dtype=np.float32)
+        opacity = 0.08 + strength * 0.22
+        for _ in range(3):
+            rr = base_r * rng.uniform(0.4, 1.0)
+            ox, oy = cx + rng.uniform(-base_r, base_r) * 0.5, cy + rng.uniform(-base_r, base_r) * 0.5
+            fill = _blob_mask((h, w), (ox, oy), (rr, rr * rng.uniform(0.7, 1.1)), float(rng.uniform(0, 180)),
+                               blur_sigma=rr * 0.5)
+            for c in range(3):
+                out[:, :, c] = out[:, :, c] * (1 - fill * opacity * 0.5) + color[c] * fill * opacity * 0.5
+
+    return np.clip(out, 0, 255).astype(np.uint8), {
+        "resolved_type": resolved_type, "center": (round(float(cx), 1), round(float(cy), 1)),
+    }
+
+
+def shadow(image: np.ndarray, strength: float, shadow_type: str, rng: np.random.Generator) -> tuple[np.ndarray, dict]:
+    """Apply an edge, corner, or fold shadow gradient."""
+    h, w = image.shape[:2]
+    resolved_type = shadow_type if shadow_type != "random" else rng.choice(["edge", "corner", "fold"])
+    out = image.astype(np.float32)
+    darkness = 0.15 + strength * 0.55
+
+    if resolved_type == "edge":
+        side = rng.choice(["top", "bottom", "left", "right"])
+        band_frac = 0.12 + strength * 0.18
+        yy, xx = np.mgrid[0:h, 0:w].astype(np.float32)
+        if side == "top":
+            grad = np.clip(1 - yy / (h * band_frac), 0, 1)
+        elif side == "bottom":
+            grad = np.clip(1 - (h - 1 - yy) / (h * band_frac), 0, 1)
+        elif side == "left":
+            grad = np.clip(1 - xx / (w * band_frac), 0, 1)
+        else:
+            grad = np.clip(1 - (w - 1 - xx) / (w * band_frac), 0, 1)
+        gain = 1 - grad * darkness
+        out *= gain[:, :, None]
+        extra = {"side": side}
+    elif resolved_type == "corner":
+        corner = rng.choice(["top_left", "top_right", "bottom_left", "bottom_right"])
+        cx = 0 if "left" in corner else w
+        cy = 0 if "top" in corner else h
+        yy, xx = np.mgrid[0:h, 0:w].astype(np.float32)
+        dist = np.hypot(xx - cx, yy - cy)
+        radius = 0.55 * min(w, h) * (0.7 + strength * 0.6)
+        grad = np.clip(1 - dist / radius, 0, 1)
+        gain = 1 - grad * darkness
+        out *= gain[:, :, None]
+        extra = {"corner": corner}
+    else:  # fold
+        n_folds = 1 + int(strength * 2)
+        extra_lines = []
+        for _ in range(n_folds):
+            vertical = bool(rng.integers(0, 2))
+            pos = rng.uniform(0.25, 0.75)
+            width = (0.015 + strength * 0.03) * (w if vertical else h)
+            yy, xx = np.mgrid[0:h, 0:w].astype(np.float32)
+            axis = xx if vertical else yy
+            center = pos * (w if vertical else h)
+            dist = np.abs(axis - center)
+            grad = np.clip(1 - dist / max(width, 1.0), 0, 1)
+            gain = 1 - grad * darkness * 0.7
+            out *= gain[:, :, None]
+            extra_lines.append({"vertical": vertical, "position_frac": round(float(pos), 3)})
+        extra = {"folds": extra_lines}
+
+    return np.clip(out, 0, 255).astype(np.uint8), {"resolved_type": resolved_type, **extra}
+
+
+def crumple(
+    image: np.ndarray, strength: float, wrinkle_density: float, rng: np.random.Generator,
+    companion: "np.ndarray | None" = None,
+) -> tuple[np.ndarray, dict, "np.ndarray | None"]:
+    """Simulate paper crumpling: subtle geometric displacement plus faint wrinkle-line shading.
+
+    ``companion``, if given, gets the exact same geometric displacement
+    remap applied (using the identical ``map_x``/``map_y`` computed from
+    this call's own random draws, before any further rng consumption for
+    shading/wrinkle-lines below) -- no shading, since only spatial
+    alignment matters for a baseline reference image, not tone. See
+    :func:`page_tilt`'s ``companion`` docstring for why this exists.
+    """
+    h, w = image.shape[:2]
+    field_x = smooth_noise_field((h, w), rng, blur_frac=0.06) - 0.5
+    field_y = smooth_noise_field((h, w), rng, blur_frac=0.06) - 0.5
+    amplitude = strength * min(w, h) * 0.015
+
+    yy, xx = np.mgrid[0:h, 0:w].astype(np.float32)
+    map_x = xx + field_x * amplitude
+    map_y = yy + field_y * amplitude
+    warped = cv2.remap(image, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
+
+    warped_companion = None
+    if companion is not None:
+        warped_companion = cv2.remap(companion, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
+
+    shade_field = smooth_noise_field((h, w), rng, blur_frac=0.03)
+    n_lines = int(3 + wrinkle_density * 12)
+    line_mask = np.zeros((h, w), dtype=np.float32)
+    for _ in range(n_lines):
+        x1, y1 = rng.uniform(0, w), rng.uniform(0, h)
+        angle = rng.uniform(0, 2 * np.pi)
+        length = rng.uniform(0.15, 0.5) * min(w, h)
+        x2, y2 = x1 + np.cos(angle) * length, y1 + np.sin(angle) * length
+        cv2.line(line_mask, (int(x1), int(y1)), (int(x2), int(y2)), 1.0, thickness=1)
+    line_mask = cv2.GaussianBlur(line_mask, (0, 0), sigmaX=1.0)
+
+    shading = (shade_field - 0.5) * strength * 0.25 - line_mask * strength * 0.18
+    out = warped.astype(np.float32) * (1 + shading[:, :, None])
+    return (
+        np.clip(out, 0, 255).astype(np.uint8),
+        {"wrinkle_lines": n_lines, "displacement_amplitude_px": round(float(amplitude), 3)},
+        np.clip(warped_companion, 0, 255).astype(np.uint8) if warped_companion is not None else None,
+    )
+
+
+_SEPIA_MATRIX = np.array([
+    [0.393, 0.769, 0.189],
+    [0.349, 0.686, 0.168],
+    [0.272, 0.534, 0.131],
+], dtype=np.float32)
+
+
+def discoloration(image: np.ndarray, strength: float, tone: str, rng: np.random.Generator) -> tuple[np.ndarray, dict]:
+    """Apply an overall color-cast discoloration: yellowing, greying, or sepia toning."""
+    resolved_tone = tone if tone != "random" else rng.choice(["yellow", "grey", "sepia"])
+    img_f = image.astype(np.float32)
+
+    if resolved_tone == "yellow":
+        tint = np.array([1.0 + strength * 0.12, 1.0 + strength * 0.04, 1.0 - strength * 0.28], dtype=np.float32)
+        out = img_f * tint[None, None, :]
+    elif resolved_tone == "grey":
+        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY).astype(np.float32)
+        gray3 = np.repeat(gray[:, :, None], 3, axis=2)
+        out = img_f * (1 - strength * 0.6) + gray3 * (strength * 0.6)
+    else:  # sepia
+        sepia = img_f @ _SEPIA_MATRIX.T
+        out = img_f * (1 - strength * 0.7) + sepia * (strength * 0.7)
+
+    unevenness = smooth_noise_field(image.shape[:2], rng, blur_frac=0.05)
+    gain = 1 - (1 - unevenness) * strength * 0.08
+    out *= gain[:, :, None]
+
+    return np.clip(out, 0, 255).astype(np.uint8), {"resolved_tone": resolved_tone}
+
+
+def page_tilt(
+    image: np.ndarray, strength: float, rng: np.random.Generator, companion: "np.ndarray | None" = None,
+) -> tuple[np.ndarray, dict, "np.ndarray | None"]:
+    """Rotate the whole page by a small angle, simulating a crooked scanner feed.
+
+    ``companion``, if given, gets the exact same rotation matrix applied
+    (no additional randomness/shading) -- used to keep a baseline
+    reference image spatially aligned with the rotated document, so ink
+    detection's "already dark in the blank template" exclusion still
+    works after a geometric transform instead of having to be skipped
+    entirely (see :func:`~src.analysis.geometry_analyzer._mask_already_dark_in_baseline`).
+    """
+    h, w = image.shape[:2]
+    angle = float(rng.uniform(-1.0, 1.0) * (1.5 + strength * 6.5))
+    matrix = cv2.getRotationMatrix2D((w / 2.0, h / 2.0), angle, 1.0)
+    rotated = cv2.warpAffine(image, matrix, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
+    rotated_companion = None
+    if companion is not None:
+        rotated_companion = cv2.warpAffine(companion, matrix, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
+    return rotated, {"tilt_deg": round(angle, 3)}, rotated_companion
+
+
+def dust(image: np.ndarray, strength: float, rng: np.random.Generator) -> tuple[np.ndarray, dict]:
+    """Scatter small light/dark specks simulating dust and sensor debris."""
+    h, w = image.shape[:2]
+    out = image.astype(np.float32)
+    n_specks = int((h * w) * 0.00003 * (1 + strength * 6))
+    xs = rng.integers(0, w, size=n_specks)
+    ys = rng.integers(0, h, size=n_specks)
+    for x, y in zip(xs, ys):
+        r = int(rng.integers(1, 3))
+        val = float(rng.choice([255.0, 40.0]))
+        cv2.circle(out, (int(x), int(y)), r, (val, val, val), -1)
+    return np.clip(out, 0, 255).astype(np.uint8), {"speck_count": int(n_specks)}
+
+
+def scanner_streaks(image: np.ndarray, strength: float, rng: np.random.Generator) -> tuple[np.ndarray, dict]:
+    """Add faint vertical scan-line streaks, simulating a dirty scanner sensor/glass."""
+    h, w = image.shape[:2]
+    out = image.astype(np.float32)
+    n_streaks = 1 + int(strength * 6)
+    xs = sorted(int(v) for v in rng.integers(0, w, size=n_streaks))
+    for x in xs:
+        width = int(rng.integers(1, 3))
+        delta = float(rng.uniform(-1, 1)) * (10 + strength * 35)
+        x1, x2 = max(x - width, 0), min(x + width, w)
+        out[:, x1:x2, :] += delta
+    return np.clip(out, 0, 255).astype(np.uint8), {"streak_count": n_streaks, "positions_x": xs}
+
+
diff --git a/unified_pipeline/src/augmentation/signature_effects.py b/unified_pipeline/src/augmentation/signature_effects.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec1ff9d2fdff4e58eef109e49afb01846ff77348
--- /dev/null
+++ b/unified_pipeline/src/augmentation/signature_effects.py
@@ -0,0 +1,106 @@
+"""Custom augmentation effects operating on the RGBA signature layer.
+
+These act on the placed signature *before* it is blended onto the document,
+so they affect only the ink itself (stroke geometry, ink density) rather
+than the surrounding paper.
+"""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from src.utils.transform_utils import rotate_and_scale_rgba, smooth_noise_field
+
+
+def stroke_morphology(rgba: np.ndarray, strength: float, thicken: bool) -> np.ndarray:
+    """Thicken or thin ink strokes via morphological dilation/erosion of the alpha channel."""
+    ksize = max(1, int(round(1 + strength * 5)))
+    ksize = ksize + 1 if ksize % 2 == 0 else ksize
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ksize, ksize))
+    alpha = rgba[:, :, 3]
+    op = cv2.dilate if thicken else cv2.erode
+    new_alpha = op(alpha, kernel, iterations=1)
+    out = rgba.copy()
+    out[:, :, 3] = new_alpha
+    return out
+
+
+def motion_blur_signature(rgba: np.ndarray, strength: float, angle_deg: float) -> np.ndarray:
+    """Directional (hand-shake style) motion blur applied to the signature stamp."""
+    length = max(3, int(round(3 + strength * 16)))
+    kernel = np.zeros((length, length), dtype=np.float32)
+    kernel[length // 2, :] = 1.0
+    center = (length / 2 - 0.5, length / 2 - 0.5)
+    rot = cv2.getRotationMatrix2D(center, angle_deg, 1.0)
+    kernel = cv2.warpAffine(kernel, rot, (length, length))
+    kernel_sum = kernel.sum()
+    if kernel_sum > 1e-6:
+        kernel /= kernel_sum
+
+    out = rgba.astype(np.float32)
+    for c in range(4):
+        out[:, :, c] = cv2.filter2D(out[:, :, c], -1, kernel, borderType=cv2.BORDER_CONSTANT)
+    return np.clip(out, 0, 255).astype(np.uint8)
+
+
+def gaussian_blur_signature(rgba: np.ndarray, sigma: float) -> np.ndarray:
+    """Isotropic blur of the signature stamp (softens edges, simulates scan blur/feathering)."""
+    if sigma <= 0:
+        return rgba
+    blurred = cv2.GaussianBlur(rgba.astype(np.float32), (0, 0), sigmaX=sigma)
+    return np.clip(blurred, 0, 255).astype(np.uint8)
+
+
+def ink_fading(rgba: np.ndarray, strength: float, rng: np.random.Generator) -> np.ndarray:
+    """Fade ink unevenly, simulating a pen running low on ink mid-stroke."""
+    h, w = rgba.shape[:2]
+    if h < 2 or w < 2:
+        return rgba
+    field = smooth_noise_field((h, w), rng, blur_frac=0.15)
+    multiplier = 1.0 - strength * (1.0 - field) * 0.9
+    multiplier = np.clip(multiplier, 0.05, 1.0)
+    out = rgba.copy()
+    out[:, :, 3] = np.clip(out[:, :, 3].astype(np.float32) * multiplier, 0, 255).astype(np.uint8)
+    return out
+
+
+def ink_bleed(rgba: np.ndarray, strength: float) -> np.ndarray:
+    """Simulate ink bleeding/feathering into paper fibers around stroke edges."""
+    ksize = max(1, int(round(1 + strength * 4)))
+    ksize = ksize + 1 if ksize % 2 == 0 else ksize
+    alpha = rgba[:, :, 3].astype(np.float32)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ksize, ksize))
+    dilated = cv2.dilate(alpha, kernel, iterations=1)
+    halo = cv2.GaussianBlur(dilated, (0, 0), sigmaX=max(ksize * 0.6, 0.8))
+    bled = np.maximum(alpha, halo * (0.35 + 0.35 * strength))
+    out = rgba.copy()
+    out[:, :, 3] = np.clip(bled, 0, 255).astype(np.uint8)
+    return out
+
+
+def _fit_to_canvas(rgba: np.ndarray, canvas_h: int, canvas_w: int) -> np.ndarray:
+    """Center-crop/pad ``rgba`` back to ``(canvas_h, canvas_w)``.
+
+    ``rotate_and_scale_rgba`` auto-expands its output to avoid clipping,
+    which is correct for a standalone stamp but not here: these jitter
+    effects run on the full-canvas placement layer, and every downstream
+    stage (blending, geometry detection) assumes that layer stays exactly
+    canvas-sized.
+    """
+    h, w = rgba.shape[:2]
+    if h == canvas_h and w == canvas_w:
+        return rgba
+    out = np.zeros((canvas_h, canvas_w, 4), dtype=rgba.dtype)
+    src_x1, src_y1 = max((w - canvas_w) // 2, 0), max((h - canvas_h) // 2, 0)
+    src = rgba[src_y1:src_y1 + min(h, canvas_h), src_x1:src_x1 + min(w, canvas_w)]
+    sh, sw = src.shape[:2]
+    dst_x1, dst_y1 = max((canvas_w - w) // 2, 0), max((canvas_h - h) // 2, 0)
+    out[dst_y1:dst_y1 + sh, dst_x1:dst_x1 + sw] = src
+    return out
+
+
+def jitter_rotation(rgba: np.ndarray, extra_deg: float) -> np.ndarray:
+    """Apply a small extra rotation on top of the base placement rotation."""
+    h, w = rgba.shape[:2]
+    return _fit_to_canvas(rotate_and_scale_rgba(rgba, extra_deg, 1.0), h, w)
diff --git a/unified_pipeline/src/config/__init__.py b/unified_pipeline/src/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6ab4775e8b62710d3b6d0e865cd016570d156d7
--- /dev/null
+++ b/unified_pipeline/src/config/__init__.py
@@ -0,0 +1,6 @@
+"""Configuration schema (Pydantic) and OmegaConf-based YAML loading."""
+
+from src.config.schema import PipelineConfig
+from src.config.loader import load_config
+
+__all__ = ["PipelineConfig", "load_config"]
diff --git a/unified_pipeline/src/config/loader.py b/unified_pipeline/src/config/loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7f0971e73e14c338f3db388bfdd0bfe8b2516a7
--- /dev/null
+++ b/unified_pipeline/src/config/loader.py
@@ -0,0 +1,70 @@
+"""OmegaConf-based configuration loading, merging, and Pydantic validation.
+
+Supports:
+
+* A base YAML file (typically ``configs/default.yaml``).
+* An optional user YAML file merged on top of the base.
+* Dot-list CLI overrides (``key.subkey=value``), e.g. as produced by
+  ``OmegaConf.from_dotlist(["placement.rotation_deg.min=-5"])``.
+
+The merged structure is converted to a plain Python container and validated
+against :class:`~src.config.schema.PipelineConfig`, so any typo or
+out-of-range value fails fast with a clear error instead of silently being
+ignored.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from omegaconf import OmegaConf
+
+from src.config.schema import PipelineConfig
+
+
+def _default_config_path() -> Path:
+    return Path(__file__).resolve().parents[2] / "configs" / "default.yaml"
+
+
+def load_config(
+    config_path: "str | Path | None" = None,
+    overrides: "list[str] | None" = None,
+) -> PipelineConfig:
+    """Load, merge, and validate the pipeline configuration.
+
+    Args:
+        config_path: Path to a user YAML config. If ``None``, only the
+            built-in ``configs/default.yaml`` is used.
+        overrides: Optional list of ``"dotted.key=value"`` CLI-style
+            overrides, applied last (highest precedence).
+
+    Returns:
+        A fully validated :class:`~src.config.schema.PipelineConfig`.
+
+    Raises:
+        pydantic.ValidationError: If the merged configuration is invalid.
+        FileNotFoundError: If ``config_path`` is given but does not exist.
+    """
+    base = OmegaConf.load(_default_config_path())
+
+    merged = base
+    if config_path is not None:
+        config_path = Path(config_path)
+        if not config_path.exists():
+            raise FileNotFoundError(f"Config file not found: {config_path}")
+        user_conf = OmegaConf.load(config_path)
+        merged = OmegaConf.merge(base, user_conf)
+
+    if overrides:
+        cli_conf = OmegaConf.from_dotlist(overrides)
+        merged = OmegaConf.merge(merged, cli_conf)
+
+    resolved = OmegaConf.to_container(merged, resolve=True)
+    return PipelineConfig.model_validate(resolved)
+
+
+def save_resolved_config(config: PipelineConfig, path: "str | Path") -> None:
+    """Write a fully-resolved configuration (as actually used) to YAML for provenance."""
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    OmegaConf.save(OmegaConf.create(config.model_dump()), path)
diff --git a/unified_pipeline/src/config/schema.py b/unified_pipeline/src/config/schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..36dc1d405bbbbc8ed88a66aa1e6f0dd89b8347e5
--- /dev/null
+++ b/unified_pipeline/src/config/schema.py
@@ -0,0 +1,330 @@
+"""Pydantic configuration schema for the synthetic document generation pipeline.
+
+Every user-facing parameter is validated here. Numeric placement/augmentation
+parameters accept either a fixed scalar or a ``{min, max}`` range (resolved
+per-sample by a seeded RNG, see :mod:`src.utils.random_utils`).
+
+The individual augmentation effects requested by the product spec are
+consolidated into configurable *families* where an effect is a strict
+specialization of another (paper stains + water stains -> ``stain`` with a
+``type`` selector; edge/corner/fold shadows -> ``shadow`` with a ``type``
+selector; crumpling + wrinkles -> ``crumple``; general discoloration + paper
+yellowing -> ``discoloration`` with a ``tone`` selector). This keeps the
+config surface coherent while still reaching every requested visual effect.
+"""
+
+from __future__ import annotations
+
+from typing import Literal, Union
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+
+NumericOrRange_T = Union[float, "Range"]
+
+
+class Range(BaseModel):
+    """An inclusive ``[min, max]`` range a value is uniformly sampled from."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    min: float
+    max: float
+
+    @model_validator(mode="after")
+    def _check_order(self) -> "Range":
+        if self.max < self.min:
+            raise ValueError(f"Range max ({self.max}) must be >= min ({self.min})")
+        return self
+
+
+NumericOrRange = Union[float, Range]
+
+
+class AugmentationParam(BaseModel):
+    """Base schema shared by every augmentation effect.
+
+    Extra keys are allowed so effect-specific parameters (e.g. ``type`` for
+    :class:`ShadowParam`, ``tone`` for discoloration) can be declared inline
+    in YAML without needing a bespoke subclass for every single effect.
+    """
+
+    model_config = ConfigDict(extra="allow")
+
+    enabled: bool = False
+    strength: NumericOrRange = 0.0
+    probability: float = Field(default=1.0, ge=0.0, le=1.0)
+    seed: "int | None" = None
+
+    @field_validator("strength")
+    @classmethod
+    def _strength_bounds(cls, v: NumericOrRange) -> NumericOrRange:
+        if isinstance(v, (int, float)) and not (0.0 <= float(v) <= 1.0):
+            raise ValueError("strength must be within [0, 1]")
+        return v
+
+
+class ShadowParam(AugmentationParam):
+    type: Literal["edge", "corner", "fold", "random"] = "random"
+
+
+class StainParam(AugmentationParam):
+    type: Literal["paper", "water", "random"] = "random"
+
+
+class DiscolorationParam(AugmentationParam):
+    tone: Literal["yellow", "grey", "sepia", "random"] = "random"
+
+
+class CrumpleParam(AugmentationParam):
+    wrinkle_density: NumericOrRange = 0.4
+
+
+class JpegCompressionParam(AugmentationParam):
+    min_quality: int = Field(default=35, ge=1, le=100)
+    max_quality: int = Field(default=90, ge=1, le=100)
+
+
+class AugmentationsConfig(BaseModel):
+    """The full family of configurable document/signature augmentations."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    rotation: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    stroke_dilation: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    motion_blur: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    gaussian_blur: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    perspective: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    scanner_noise: AugmentationParam = Field(default_factory=lambda: AugmentationParam(enabled=True, strength=0.06, probability=1.0))
+    salt_pepper: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    jpeg_compression: JpegCompressionParam = Field(default_factory=lambda: JpegCompressionParam())
+    contrast: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    gamma: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    coffee_stain: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    stain: StainParam = Field(default_factory=lambda: StainParam())
+    shadow: ShadowParam = Field(default_factory=lambda: ShadowParam())
+    crumple: CrumpleParam = Field(default_factory=lambda: CrumpleParam())
+    discoloration: DiscolorationParam = Field(default_factory=lambda: DiscolorationParam())
+    page_tilt: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    dust: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    scanner_streaks: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    ink_fading: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+    ink_bleed: AugmentationParam = Field(default_factory=lambda: AugmentationParam())
+
+    def items(self):
+        """Yield ``(name, AugmentationParam)`` pairs for every configured effect."""
+        for name in self.model_fields:
+            yield name, getattr(self, name)
+
+
+class PlacementConfig(BaseModel):
+    """Controls where and how the signature is positioned inside its target box."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    x_offset: NumericOrRange = 0.0
+    y_offset: NumericOrRange = 0.0
+    rotation_deg: NumericOrRange = 0.0
+    scale_factor: NumericOrRange = 0.9
+    anchor_point: Literal[
+        "center", "top_left", "top_right", "bottom_left", "bottom_right",
+        "top_center", "bottom_center", "left_center", "right_center",
+    ] = "center"
+    margin: float = Field(default=0.06, ge=0.0, le=0.45)
+    misplacement_probability: float = Field(default=0.0, ge=0.0, le=1.0)
+    misplacement_strength: NumericOrRange = 0.3
+    allow_partial_outside: bool = False
+    # 28px, not the deck's 16px (see placement_engine.py's own docstring history
+    # for the original 24px-vs-16px measurement) -- raised again 2026-08-11 after
+    # a real signature placed into a genuine 18px "sign here" line was measured
+    # at each candidate floor: 24px -> 15.1% fully-opaque ink, 28px -> 16.8%,
+    # with only a mild overflow cost (median 4% at 28px vs 0% at 24px) --
+    # pushing further (32px+) escalates overflow much faster than legibility
+    # gain (20%+ median overflow by 32px) so 28px was kept as the balance point.
+    min_signature_height_px: float = Field(default=28.0, gt=0.0)
+
+
+class RenderingConfig(BaseModel):
+    """Controls how the signature layer is composited onto the document."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    blend_mode: Literal["multiply", "darken", "alpha"] = "multiply"
+    ink_opacity: NumericOrRange = 0.88
+    blur_sigma: NumericOrRange = 0.35
+    feather_px: NumericOrRange = 1.2
+    scanner_noise: NumericOrRange = 0.02
+    anti_alias: bool = True
+
+
+class SignerConfig(BaseModel):
+    """Optional overrides for the assigned signer identity."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    name: "str | None" = None
+    role: "str | None" = None
+
+
+class SignatureSelectionConfig(BaseModel):
+    """Controls how a signature image is drawn from the discovered dataset."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    source_filter: Literal["genuine", "forged", "any"] = "genuine"
+    signature_id: "str | None" = None
+
+
+class DocumentConfig(BaseModel):
+    """Selects which template (and page) to render."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    template: "str | None" = None
+    page: "int | None" = None
+    # Cosmetic (non-scored) field filling -- SWIFT/bank code, NRIC, UEN,
+    # amounts, dates. Only meaningful for procedurally-drawn documents:
+    # scanned templates are static photos with no text-region annotations
+    # to inject synthetic text into. See src/cosmetics/cosmetic_fields.py.
+    enable_cosmetic_fields: bool = False
+
+
+class QualityConfig(BaseModel):
+    """Document quality-flag inputs (feed into the ``quality`` metadata block)."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    case_type: "str | None" = None
+    notes: "str | None" = None
+    scanner_quality: NumericOrRange = 0.85
+    paper_quality: NumericOrRange = 0.85
+    render_quality: NumericOrRange = 0.85
+
+
+class DifficultyWeights(BaseModel):
+    """Weights and tier thresholds for the difficulty score.
+
+    Defaults match the specification exactly:
+    ``0.35*misplacement + 0.20*whitespace + 0.20*faintness + 0.15*smallness + 0.10*low_texture``.
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    misplacement: float = 0.35
+    whitespace: float = 0.20
+    faintness: float = 0.20
+    smallness: float = 0.15
+    low_texture: float = 0.10
+    easy_threshold: float = 0.33
+    hard_threshold: float = 0.66
+
+    # Reference constants each component is normalized against before
+    # weighting. Defaults match the spec deck's generic literal values
+    # (verified against the scanned bank-document templates). Procedural
+    # templates measure much smaller/lighter real signature ink than these
+    # generic references assume, so `configs/procedural_templates.yaml`
+    # overrides these three to values refit against that template family's
+    # own measured proportions -- see `unified_pipeline/README.md`.
+    smallness_reference_area_ratio: float = 0.04
+    faintness_dark_ref: float = 90.0
+    faintness_light_ref: float = 210.0
+    low_texture_reference_stddev: float = 60.0
+
+    @model_validator(mode="after")
+    def _check_weights(self) -> "DifficultyWeights":
+        total = self.misplacement + self.whitespace + self.faintness + self.smallness + self.low_texture
+        if abs(total - 1.0) > 1e-6:
+            raise ValueError(f"Difficulty weights must sum to 1.0, got {total}")
+        return self
+
+
+class PathsConfig(BaseModel):
+    """Filesystem locations for inputs/outputs."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    image_dir: str = "templates/scanned"
+    dataset_dir: str = "dataset"
+    annotations_dir: str = "annotations"
+    output_dir: str = "output"
+    cache_dir: str = "templates/procedural"
+
+    # Additive signature-image sources beyond `dataset_dir` (which already
+    # covers CEDAR genuine/forged + the curated hindi_bengali sample). Each
+    # is optional -- `None`/missing-on-disk sources are skipped gracefully,
+    # same policy as the original BHSig260 wiring in projectA. See
+    # SignatureDataset.discover() in src/signatures/signature_dataset.py.
+    signet_dir: "str | None" = None
+    gpds_dir: "str | None" = None
+    bhsig260_hindi_dir: "str | None" = None
+    bhsig260_bengali_dir: "str | None" = None
+
+    # Which TemplateProvider(s) supply documents: pre-made scanned images
+    # with hand-measured box annotations ("scanned", the original behavior
+    # of this repo), matplotlib-drawn documents ("procedural", ported from
+    # projectA/utils/templatemaker.py), or both merged into one id space
+    # ("both"). See src/templates/template_provider.py.
+    template_provider: Literal["scanned", "procedural", "both"] = "scanned"
+
+    # Scanned template ids to leave out of the pool entirely (e.g.
+    # ["bank_doc_08"]). Ids are assigned before filtering, so excluding one
+    # never renumbers/shifts any other template's id -- see
+    # src/templates/template_loader.py::TemplateLoader.
+    excluded_template_ids: "list[str]" = Field(default_factory=list)
+
+
+class GenerationConfig(BaseModel):
+    """Batch-generation controls."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    count: int = Field(default=10, ge=1)
+    workers: int = Field(default=1, ge=1)
+    save_visualization: bool = False
+    # Before/after degradation comparison PNG per sample (clean render vs.
+    # final look, labeled banners) -- ported from projectA's
+    # metrics_overlay.py::build_before_after_preview(). See
+    # src/visualization/before_after_preview.py.
+    save_before_after_preview: bool = False
+    overwrite: bool = False
+    run_validation: bool = True
+    start_index: int = Field(default=1, ge=0)
+    filename_prefix: str = "case"
+    filename_digits: int = Field(default=6, ge=1)
+
+    # Difficulty-score targeting: request a specific numeric score (e.g.
+    # 0.55) instead of a coarse easy/medium/hard tier -- ported from
+    # projectA's cli.py --difficulty {easy,medium,hard} retry-loop
+    # (generate_case_at_difficulty()), generalized to a float target since
+    # this pipeline's difficulty score is already continuous. `None` = off,
+    # natural generation (default). See GeneratorContext.run_sample_targeting_difficulty().
+    target_difficulty: "float | None" = Field(default=None, ge=0.0, le=1.0)
+    difficulty_tolerance: float = Field(default=0.05, ge=0.0, le=1.0)
+    difficulty_max_attempts: int = Field(default=5, ge=1)
+
+
+class ValidationConfig(BaseModel):
+    """Controls strictness of the post-generation ValidationEngine."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    strict: bool = False
+    max_coordinate_epsilon: float = 0.5
+
+
+class PipelineConfig(BaseModel):
+    """Top-level configuration for a full generation run."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    seed: int = 1234
+    paths: PathsConfig = Field(default_factory=PathsConfig)
+    generation: GenerationConfig = Field(default_factory=GenerationConfig)
+    document: DocumentConfig = Field(default_factory=DocumentConfig)
+    signer: SignerConfig = Field(default_factory=SignerConfig)
+    signature_selection: SignatureSelectionConfig = Field(default_factory=SignatureSelectionConfig)
+    placement: PlacementConfig = Field(default_factory=PlacementConfig)
+    rendering: RenderingConfig = Field(default_factory=RenderingConfig)
+    augmentation: AugmentationsConfig = Field(default_factory=AugmentationsConfig)
+    quality: QualityConfig = Field(default_factory=QualityConfig)
+    difficulty: DifficultyWeights = Field(default_factory=DifficultyWeights)
+    validation: ValidationConfig = Field(default_factory=ValidationConfig)
diff --git a/unified_pipeline/src/cosmetics/__init__.py b/unified_pipeline/src/cosmetics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/unified_pipeline/src/cosmetics/cosmetic_fields.py b/unified_pipeline/src/cosmetics/cosmetic_fields.py
new file mode 100644
index 0000000000000000000000000000000000000000..4532e91928592f06244448a8cc21196f056b6038
--- /dev/null
+++ b/unified_pipeline/src/cosmetics/cosmetic_fields.py
@@ -0,0 +1,255 @@
+"""Cosmetic (non-scored) field filling for procedurally-drawn documents.
+
+Ported from ``projectA/utils/cosmetic_fields.py``. Fills in the parts of a
+bank document that make it look like a real filled-in form -- entity name,
+registration number, SWIFT/routing code, amounts, dates -- none of which
+factor into :mod:`src.analysis.difficulty_analyzer` or any other scored
+metric. Only meaningful for :class:`~src.templates.procedural_template_provider.ProceduralTemplateProvider`
+documents: scanned templates are static photos with no text-region
+annotations to inject synthetic text into (see ``paths.enable_cosmetic_fields``
+in ``src/config/schema.py``).
+
+Operates on the same ``(H, W, 3)`` uint8 RGB ``numpy`` arrays the rest of the
+pipeline uses (:mod:`src.utils.image_io`), converting to/from a PIL image
+internally since text rendering needs ``ImageDraw``.
+
+Every random draw here takes an explicit ``random.Random`` (the caller
+passes ``seed_manager.python_rng("cosmetic_fields")`` -- see
+``src/generator.py::_maybe_fill_cosmetic_fields``) rather than using the
+global ``random`` module. An earlier version used the global module
+directly, which silently broke this pipeline's core "same seed -> same
+output" guarantee for every sample with cosmetic fields enabled (invisible
+while the feature defaulted to off; surfaced as a real
+``test_run_sample_is_fully_reproducible`` failure the moment it became
+default-on).
+"""
+
+from __future__ import annotations
+
+import random
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+from src.templates.procedural_template_provider import _box_px, _to_px, get_bank_info, get_field_boxes
+
+_CHEQUE_FIELD_TARGETS_IN = {entry["field"]: entry["box_in"] for entry in get_field_boxes("Cheque Voucher")}
+
+
+def _get_font(size: int = 14, bold: bool = False) -> "ImageFont.FreeTypeFont":
+    candidates = [
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf" if bold else "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+        "/Library/Fonts/Arial Bold.ttf" if bold else "/Library/Fonts/Arial.ttf",
+        "/System/Library/Fonts/Supplemental/Arial Bold.ttf" if bold else "/System/Library/Fonts/Supplemental/Arial.ttf",
+        "C:\\Windows\\Fonts\\arialbd.ttf" if bold else "C:\\Windows\\Fonts\\arial.ttf",
+        "arialbd.ttf" if bold else "arial.ttf",
+        "DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf",
+    ]
+    for path in candidates:
+        try:
+            return ImageFont.truetype(path, size)
+        except Exception:
+            continue
+    try:
+        return ImageFont.load_default(size=size)
+    except TypeError:
+        return ImageFont.load_default()
+
+
+def _fill_name_field(draw, name):
+    x, y = _to_px(0.6, 8.5)
+    font = _get_font(24)
+    draw.text((x, y), name, fill=(25, 25, 25), font=font)
+    return {"full_legal_name": list(draw.textbbox((x, y), name, font=font))}
+
+
+def _fill_cheque_payee(draw, name):
+    x1, y1, x2, y2 = _box_px(*_CHEQUE_FIELD_TARGETS_IN["payee_name"])
+    x, y = x1 + 6, y1 + 8
+    font = _get_font(24)
+    draw.text((x, y), name, fill=(20, 20, 20), font=font)
+    return {"payee_name": list(draw.textbbox((x, y), name, font=font))}
+
+
+def generate_nric(rng: "random.Random") -> str:
+    """A plausible Singapore NRIC (S/T + 7 digits + real checksum letter)."""
+    prefix = rng.choice(["S", "T"])
+    digits = [rng.randint(0, 9) for _ in range(7)]
+    weights = [2, 7, 6, 5, 4, 3, 2]
+    total = sum(d * w for d, w in zip(digits, weights))
+    if prefix == "T":
+        total += 4
+    remainder = total % 11
+    st_table = ["J", "Z", "I", "H", "G", "F", "E", "D", "C", "B", "A"]
+    checksum_letter = st_table[remainder]
+    return f"{prefix}{''.join(str(d) for d in digits)}{checksum_letter}"
+
+
+_COMPANY_NAME_PREFIXES = [
+    "Marina Bay", "Orchard Road", "Raffles Place", "Sentosa Cove",
+    "Jurong Industrial", "Tanjong Pagar", "Bugis Junction", "Clarke Quay",
+    "Shenton Way", "Changi Business", "Woodlands", "Paya Lebar",
+]
+_COMPANY_NAME_SUFFIXES = [
+    "Holdings", "Trading", "Ventures", "Capital", "Enterprises",
+    "Investments", "Logistics", "Industries", "Resources", "Partners",
+]
+
+
+def generate_company_name(rng: "random.Random") -> str:
+    """A plausible Singapore-style corporate entity name."""
+    prefix = rng.choice(_COMPANY_NAME_PREFIXES)
+    suffix = rng.choice(_COMPANY_NAME_SUFFIXES)
+    return f"{prefix} {suffix} Pte Ltd"
+
+
+def generate_uen(rng: "random.Random") -> str:
+    """A format-correct (not checksum-validated) Singapore UEN."""
+    year = rng.randint(1990, 2026)
+    serial = rng.randint(10000, 99999)
+    check_letter = rng.choice("ABCDEFGHJKLMNPQRSTUVWXYZ")
+    return f"{year}{serial}{check_letter}"
+
+
+def _fill_corporate_cosmetic_fields(draw, rng, reg_no=None, country="Singapore", tax_jurisdiction="SG", address="12 Marina Blvd, Singapore"):
+    font = _get_font(24)
+    reg_no = reg_no or generate_nric(rng)
+    field_boxes = {}
+    for field_name, (x_in, y_in), text in [
+        ("registration_no", (4.35, 8.5), reg_no),
+        ("country_of_incorporation", (0.6, 7.85), country),
+        ("tax_jurisdiction", (4.35, 7.85), tax_jurisdiction),
+        ("registered_address", (0.6, 6.6), address),
+    ]:
+        x, y = _to_px(x_in, y_in)
+        draw.text((x, y), text, fill=(25, 25, 25), font=font)
+        field_boxes[field_name] = list(draw.textbbox((x, y), text, font=font))
+    return field_boxes
+
+
+def _fill_section2_row(draw, rng, row_index, swift, currency="SGD", amount=None, checked=True):
+    font = _get_font(24)
+    y_row = 5.4 - 0.45 * row_index
+    amount = amount or f"{rng.randint(5_000, 500_000):,}.00"
+    field_boxes = {}
+    if not checked:
+        return field_boxes
+    cb_x1, cb_y1, cb_x2, cb_y2 = _box_px(0.6, y_row, 0.72, y_row + 0.12)
+    draw.line([(cb_x1, cb_y1), (cb_x2, cb_y2)], fill=(20, 20, 20), width=2)
+    draw.line([(cb_x2, cb_y1), (cb_x1, cb_y2)], fill=(20, 20, 20), width=2)
+    field_boxes["checkbox"] = [cb_x1, cb_y1, cb_x2, cb_y2]
+    for field_name, (x_in, y_in), text in [
+        ("swift", (3.5, y_row + 0.1), swift), ("currency", (5.5, y_row + 0.1), currency), ("amount", (6.7, y_row + 0.1), amount),
+    ]:
+        x, y = _to_px(x_in, y_in)
+        draw.text((x, y), text, fill=(25, 25, 25), font=font)
+        field_boxes[field_name] = list(draw.textbbox((x, y), text, font=font))
+    return field_boxes
+
+
+def _fill_cheque_cosmetic_fields(draw, rng, date_str=None, amount_words=None, amount_num=None, bearer=False):
+    font = _get_font(24)
+    field_boxes = {}
+    # A synthetic date derived from rng, NOT datetime.date.today() -- the
+    # real wall-clock date would make this field (and therefore the whole
+    # rendered image) reproducible only within a single calendar day, not
+    # actually reproducible from the seed the way every other field here is.
+    date_str = date_str or f"{rng.randint(1, 28):02d}{rng.randint(1, 12):02d}{rng.randint(2023, 2026)}"
+
+    dx1, dy1, dx2, dy2 = _box_px(*_CHEQUE_FIELD_TARGETS_IN["date"])
+    n_chars = 8
+    cell_w = (dx2 - dx1) / n_chars
+    char_boxes = []
+    for i, ch in enumerate(date_str[:n_chars]):
+        x = int(dx1 + i * cell_w + cell_w * 0.3)
+        y = dy1 + 3
+        draw.text((x, y), ch, fill=(20, 20, 20), font=font)
+        char_boxes.append(draw.textbbox((x, y), ch, font=font))
+    if char_boxes:
+        xs = [b[0] for b in char_boxes] + [b[2] for b in char_boxes]
+        ys = [b[1] for b in char_boxes] + [b[3] for b in char_boxes]
+        field_boxes["date"] = [min(xs), min(ys), max(xs), max(ys)]
+
+    amount_num = amount_num if amount_num is not None else rng.randint(500, 50000)
+    if amount_words is None:
+        amount_words = f"{amount_num:,} DOLLARS ONLY"
+    ax1, ay1, _, _ = _box_px(*_CHEQUE_FIELD_TARGETS_IN["amount_words"])
+    x, y = ax1 + 6, ay1 + 6
+    draw.text((x, y), amount_words.upper(), fill=(20, 20, 20), font=font)
+    field_boxes["amount_words"] = list(draw.textbbox((x, y), amount_words.upper(), font=font))
+
+    amount_text = f"{amount_num:,.2f}"
+    nx1, ny1, nx2, ny2 = _box_px(*_CHEQUE_FIELD_TARGETS_IN["amount_numeric"])
+    box_h = ny2 - ny1
+    tmp_bbox = draw.textbbox((0, 0), amount_text, font=font)
+    text_h = tmp_bbox[3] - tmp_bbox[1]
+    x = nx1 + 110
+    y = ny1 + (box_h - text_h) // 2
+    draw.text((x, y), amount_text, fill=(20, 20, 20), font=font)
+    field_boxes["amount_numeric"] = list(draw.textbbox((x, y), amount_text, font=font))
+
+    if bearer:
+        x, y = _to_px(6.805, 8.005)
+        draw.line([(x, y), (x + 18, y + 18)], fill=(20, 20, 20), width=2)
+        draw.line([(x + 18, y), (x, y + 18)], fill=(20, 20, 20), width=2)
+        field_boxes["bearer_checkbox"] = [x, y, x + 18, y + 18]
+    return field_boxes
+
+
+def fill_all_cosmetic_fields(
+    document_image: "np.ndarray", template_cfg: dict, primary_person_name: str, rng: "random.Random",
+) -> "tuple[np.ndarray, dict]":
+    """Single dispatch point: fills in cosmetic fields for one document.
+
+    Args:
+        document_image: ``(H, W, 3)`` uint8 RGB array (e.g. from
+            ``load_image_rgb``), the clean procedural template BEFORE
+            signature placement.
+        template_cfg: the ``dataset_directory``-style config for this
+            template, from ``ProceduralTemplateProvider.field_boxes_for(id)``
+            (has ``bank``, ``type``, ``entity_type``).
+        primary_person_name: the signer's display name, shown as the
+            entity/payee name on "individual"-type templates.
+        rng: a seeded ``random.Random`` (e.g.
+            ``seed_manager.python_rng("cosmetic_fields")``) -- every random
+            draw in this module goes through it, never the global ``random``
+            module, so the same seed always produces the same fields.
+
+    Returns:
+        ``(document_image, field_boxes)`` -- a NEW array (the input is not
+        mutated) and a dict of ``field_name -> {"actual_box", "target_box"}``,
+        matching ``projectA``'s ``ground_truth.json`` ``metrics.field_boxes``
+        shape (``target_box`` is ``None`` where no analytic target exists).
+    """
+    pil_img = Image.fromarray(document_image.astype(np.uint8)).convert("RGB")
+    draw = ImageDraw.Draw(pil_img)
+    field_boxes: "dict[str, list]" = {}
+
+    if template_cfg["type"].lower() == "cheque voucher":
+        field_boxes.update(_fill_cheque_payee(draw, primary_person_name))
+        field_boxes.update(_fill_cheque_cosmetic_fields(draw, rng, bearer=rng.random() < 0.15))
+    else:
+        entity_type = template_cfg.get("entity_type", "individual")
+        if entity_type == "corporate":
+            entity_name = generate_company_name(rng)
+            reg_no = generate_uen(rng)
+        else:
+            entity_name = primary_person_name
+            reg_no = generate_nric(rng)
+        field_boxes.update(_fill_name_field(draw, entity_name))
+        field_boxes.update(_fill_corporate_cosmetic_fields(draw, rng, reg_no=reg_no))
+        swift = get_bank_info(template_cfg["bank"])["swift"]
+        for row_index in range(3):
+            fb = _fill_section2_row(draw, rng, row_index, swift=swift, checked=(row_index == 0))
+            field_boxes.update({f"{k}_row{row_index}": v for k, v in fb.items()})
+
+    target_boxes_in = {entry["field"]: entry["box_in"] for entry in get_field_boxes(template_cfg["type"])}
+    field_boxes = {
+        name: {
+            "actual_box": actual_box,
+            "target_box": list(_box_px(*target_boxes_in[name])) if name in target_boxes_in else None,
+        }
+        for name, actual_box in field_boxes.items()
+    }
+    return np.array(pil_img), field_boxes
diff --git a/unified_pipeline/src/cosmetics/scanned_cosmetic_fields.py b/unified_pipeline/src/cosmetics/scanned_cosmetic_fields.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55ca64703b20931011189a09dfe49784847c962
--- /dev/null
+++ b/unified_pipeline/src/cosmetics/scanned_cosmetic_fields.py
@@ -0,0 +1,96 @@
+"""Cosmetic (non-scored) name/company-name filling for SCANNED templates.
+
+Reads pre-measured field coordinates straight from the template's own
+annotation (``template.annotation.pages[N].cosmetic_fields`, see
+:class:`~src.templates.annotation_schema.CosmeticFieldAnnotation``) --
+exactly like :mod:`src.placement.placement_engine` already reads
+``signature_boxes`` from the same file, no image analysis at generation
+time. Those coordinates are measured ONCE by
+``scripts/detect_cosmetic_field_coordinates.py`` (search a hand-picked
+region for the real blank line/table-divider via the same classical-CV
+line detector already used for signature-box auto-detection, then cache
+the exact result) -- see that script's docstring for the full method.
+
+Two of the nine bundled templates (``bank_doc_01``, ``bank_doc_02``) are
+stock "sample" images with fabricated content already baked into the photo
+itself (bracketed ``[PLACEHOLDER ...]`` text, or an already-fully-filled
+specimen letter) -- there is no genuine blank field to fill on those two
+without inpainting over existing print, so their annotations simply have
+no ``cosmetic_fields`` entries and this module is a no-op for them.
+"""
+
+from __future__ import annotations
+
+import random
+from typing import TYPE_CHECKING
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+from src.cosmetics.cosmetic_fields import _get_font, generate_company_name
+
+if TYPE_CHECKING:
+    from src.templates.annotation_schema import CosmeticFieldAnnotation
+
+# Fields whose text must be GENERATED (not the signer's own name) -- keyed
+# the same as each CosmeticFieldAnnotation.field value. Anything not listed
+# here just uses the signer's display name directly (correct for every
+# "Print Name:"/"Mr. Ms. ___"/"Name of Account Holder:"-style field).
+# "company_name" (bank_doc_09's "Company Name" table cell, under ENTITY
+# DETAILS next to Registration Number) is the one field that is NOT a
+# personal-name field -- pasting a person's name there (e.g. "Nancy
+# Ivanov") under a column header literally reading "Company Name" was a
+# real bug: right position, wrong kind of text for that field.
+_GENERATED_FIELD_TEXT = {
+    "company_name": generate_company_name,
+}
+
+
+def fill_scanned_cosmetic_fields(
+    document_image: "np.ndarray",
+    cosmetic_fields: "list[CosmeticFieldAnnotation]",
+    primary_person_name: str,
+    rng: "random.Random",
+) -> "tuple[np.ndarray, dict]":
+    """Fill in this scanned template's pre-measured cosmetic fields.
+
+    Args:
+        document_image: ``(H, W, 3)`` uint8 RGB array (the loaded scanned
+            template page, before signature placement).
+        cosmetic_fields: ``template.annotation.pages[N].cosmetic_fields``
+            -- empty for templates with no genuine blank field (this
+            function is then a no-op).
+        primary_person_name: the signer's display name -- used for every
+            field except the ones in ``_GENERATED_FIELD_TEXT``.
+        rng: a seeded ``random.Random`` (e.g.
+            ``seed_manager.python_rng("cosmetic_fields")``), passed through
+            to :func:`~src.cosmetics.cosmetic_fields.generate_company_name`
+            so this stays reproducible from the pipeline's seed.
+
+    Returns:
+        ``(document_image, field_boxes)`` -- a NEW array (input untouched)
+        and a dict of ``field_name -> {"actual_box": [...], "target_box": None}``
+        for whichever fields were drawn (empty dict if ``cosmetic_fields`` is empty).
+    """
+    if not cosmetic_fields:
+        return document_image, {}
+
+    pil_img = Image.fromarray(document_image.astype(np.uint8)).convert("RGB")
+    draw = ImageDraw.Draw(pil_img)
+    h = pil_img.height
+    font = _get_font(max(14, int(h * 0.014)))
+    field_boxes: "dict[str, dict]" = {}
+
+    for spec in cosmetic_fields:
+        generator = _GENERATED_FIELD_TEXT.get(spec.field)
+        text = generator(rng) if generator else primary_person_name
+        pad = max(4, int(spec.line_w * 0.02))
+        if spec.mode == "above_line":
+            x, y = spec.line_x + pad, spec.line_y - font.size - 2
+        else:  # "below_line" -- a table row-divider; write inside the cell below it
+            x, y = spec.line_x + pad, spec.line_y + max(3, int(font.size * 0.15))
+
+        draw.text((x, y), text, fill=(25, 25, 25), font=font)
+        field_boxes[spec.field] = {"actual_box": list(draw.textbbox((x, y), text, font=font)), "target_box": None}
+
+    return np.array(pil_img), field_boxes
diff --git a/unified_pipeline/src/generator.py b/unified_pipeline/src/generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..919ac001ca9c7785b2844484991f2342fd5e8c8c
--- /dev/null
+++ b/unified_pipeline/src/generator.py
@@ -0,0 +1,1167 @@
+"""Top-level orchestration: DatasetGenerator drives the full per-sample pipeline.
+
+For every sample this wires together, in order:
+
+1. :class:`~src.templates.template_loader.TemplateLoader` -- pick a template + signature box.
+2. :class:`~src.signatures.signature_dataset.SignatureDataset` -- pick a signature image.
+3. :class:`~src.rendering.signature_preprocessor.SignaturePreprocessor` -- clean it into an RGBA stamp.
+4. :class:`~src.placement.placement_engine.PlacementEngine` -- fit/rotate/position it.
+5. :class:`~src.augmentation.augmentation_engine.AugmentationEngine` -- signature-layer effects.
+6. :class:`~src.rendering.renderer.Renderer` -- blend onto the document.
+7. :class:`~src.augmentation.augmentation_engine.AugmentationEngine` -- document-level effects.
+8. :class:`~src.analysis.geometry_analyzer.GeometryAnalyzer` -- detect + measure.
+9. :class:`~src.analysis.difficulty_analyzer.DifficultyAnalyzer` -- score.
+10. :class:`~src.metadata.metadata_exporter.MetadataExporter` -- assemble + write JSON.
+11. :class:`~src.analysis.validation_engine.ValidationEngine` -- verify.
+
+A single :class:`GeneratorContext` holds all stateless-between-calls engine
+instances for one process; :class:`DatasetGenerator` either drives it
+directly (``workers=1``) or fans it out across a
+:class:`concurrent.futures.ProcessPoolExecutor` pool, where each worker
+process builds exactly one context via ``_init_worker`` and reuses it for
+every sample it is assigned. Because every random draw is derived purely
+from ``(master_seed, sample_index, stage)``, results are identical
+regardless of worker count or scheduling order.
+"""
+
+from __future__ import annotations
+
+import csv
+import shutil
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+from src.analysis.difficulty_analyzer import DifficultyAnalyzer
+from src.analysis.geometry_analyzer import GeometryAnalyzer
+from src.analysis.validation_engine import ValidationEngine, ValidationReport, summarize
+from src.augmentation.augmentation_engine import AugmentationEngine
+from src.config.schema import DifficultyWeights, PipelineConfig, PlacementConfig, Range
+from src.cosmetics.cosmetic_fields import fill_all_cosmetic_fields
+from src.cosmetics.scanned_cosmetic_fields import fill_scanned_cosmetic_fields
+from src.metadata.metadata_exporter import MetadataExporter
+from src.placement.placement_engine import PlacementEngine, PlacementResult
+from src.rendering.printed_text import render_printed_name_stamp
+from src.rendering.renderer import Renderer
+from src.rendering.signature_preprocessor import SignaturePreprocessor
+from src.signatures.signature_dataset import SignatureDataset, SignatureRecord
+from src.templates.annotation_schema import SignatureBoxAnnotation, TemplatePageAnnotation
+from src.templates.procedural_template_provider import ProceduralTemplateProvider
+from src.templates.template_loader import Template, TemplateLoader
+from src.templates.template_provider import CompositeTemplateProvider, TemplateProvider, template_kind
+from src.utils.image_io import load_image_any, load_image_rgb, save_image_rgb
+from src.utils.logging_utils import get_logger
+from src.utils.names import generate_name, generate_role
+from src.utils.random_utils import SeedManager, resolve_scalar_or_range
+from src.verification.case_types import CASE_TABLE, CaseSpec
+from src.verification.registry import SignerIdentityRegistry
+from src.visualization.before_after_preview import save_before_after_preview
+from src.visualization.visualizer import save_visualization
+
+logger = get_logger(__name__)
+
+INK_COLOR_POOL: tuple[tuple[int, int, int], ...] = (
+    (15, 15, 20),    # black
+    (18, 22, 58),    # blue-black
+    (24, 34, 96),    # ballpoint blue
+    (12, 12, 14),    # deep black
+)
+
+# DifficultyWeights' schema defaults for these fields are the deck's
+# literal, generic reference constants (0.04 / 90 / 210 / 60) -- measured
+# (see configs/procedural_templates.yaml's own history) to peg smallness/
+# faintness near their ceiling for almost any real signature on this
+# pipeline's own templates, especially procedural ones (real signature area
+# is ~0.2-1.5% of the page vs. the deck's assumed 4% "normal"). Previously
+# this recalibration only applied if the whole run used
+# `--config configs/procedural_templates.yaml`, which meant a
+# `template_provider: both` batch (or the plain CLI default) silently used
+# the deck-literal, too-aggressive constants for procedural samples anyway.
+# Applied automatically per-sample below, keyed on template_kind(), instead.
+#
+# faintness_dark_ref and low_texture_reference_stddev refit 2026-08-11
+# against a fresh 60-sample natural draw of real procedural output
+# (mean_dark_intensity: p10=164.0/median=180.6/p90=195.7; gray_stddev:
+# p10=25.3/median=34.6/p90=46.5) -- the prior faintness_dark_ref (184) sat
+# ABOVE the real median (180.6), so `clamp01((dark_intensity-dark_ref)/...)`
+# floored to exactly 0.000 for every sample in a fresh measurement (20% of
+# the total score, contributing nothing, discriminating nothing, for the
+# entire procedural template family). Re-anchored to real p10 (~164, rounded
+# to 165) so faintness has genuine spread across the real distribution
+# instead of a constant zero: median sample now scores ~0.30, darkest
+# decile ~0, lightest observed (~208) ~0.79. low_texture_reference_stddev
+# was never split from the deck-literal default at all (only smallness/
+# faintness were, historically) despite the same underlying asymmetry
+# existing here too -- anchored to real p90 (~46.5, rounded to 46) so the
+# best-textured decile of real samples can reach low_texture=0, the same
+# treatment already given to smallness_reference_area_ratio.
+_PROCEDURAL_DIFFICULTY_RECALIBRATION = {
+    "smallness_reference_area_ratio": 0.003,
+    "faintness_dark_ref": 165.0,
+    "faintness_light_ref": 220.0,
+    "low_texture_reference_stddev": 46.0,
+}
+
+# configs/default.yaml's own shipped baseline for placement.scale_factor --
+# NOT the bare schema class default (a flat 0.9), the actual range every
+# real run resolves to unless a config/CLI override says otherwise. Used
+# below to detect "nobody customized this" without relying on
+# model_fields_set, which can't tell configs/default.yaml's own baseline
+# apart from a genuine user override (both look identical once merged).
+_DEFAULT_SCALE_FACTOR = Range(min=0.78, max=0.95)
+
+# scale_factor=1.15 (flat), not the ~0.78-0.95 shipped range -- procedural's
+# boxes (200x80px) have real headroom scanned's cramped real "sign here"
+# lines don't, measured 2026-08-11 across 20 real placements per candidate:
+# scale_factor=1.1 already gave a meaningful legibility gain (opaque-ink
+# fraction 0.238 -> 0.280) with ZERO overflow (median and p90 both 0%); 1.3
+# starts trading further gains for real overflow (6-7%). 1.15 sits just past
+# the confirmed free win. Scanned was tested too (0.9 through 1.2) and
+# showed NO change at all in that range -- `min_signature_height_px` (see
+# PlacementConfig), not scale_factor, is the binding constraint there, so
+# scanned keeps configs/default.yaml's own range unchanged.
+_PROCEDURAL_PLACEMENT_RECALIBRATION = {
+    "scale_factor": 1.15,
+}
+
+# perspective/page_tilt/crumple spatially warp the whole page, which used
+# to force baseline-diff ink detection to be skipped entirely for any
+# sample where one fired (a pixel in the final image no longer sat at the
+# same (x, y) it occupied in the clean baseline, so a naive diff would
+# compare misaligned pixels) -- this silently let large blocks of printed
+# text get misdetected as "ink" whenever a search box's expanded region
+# rotated/warped into them, corrupting difficulty scores for those samples.
+# Fixed for the SCORED target box: AugmentationEngine.apply_to_document()'s
+# optional `baseline_image` param now applies the exact same geometric
+# transform to a baseline companion image too, so the `warped_baseline` used
+# below stays pixel-aligned with `final_image` even after a transform --
+# baseline exclusion no longer needs to be skipped there. This constant is
+# still used by compute_extra_signature_overlays() below, which re-detects
+# OTHER (non-scored, preview-only) boxes after generation already finished
+# and doesn't have a correspondingly-warped baseline on hand -- skipping
+# baseline exclusion for those is an accepted, lower-stakes fallback since
+# they never feed difficulty/verdict, only the before/after preview image.
+_GEOMETRIC_DOCUMENT_EFFECTS = ("perspective", "page_tilt", "crumple")
+
+
+@dataclass
+class SampleOutcome:
+    """Lightweight, picklable result of generating one sample (used across process boundaries)."""
+
+    sample_id: str
+    success: bool
+    error: "str | None" = None
+    difficulty_tier: "str | None" = None
+    difficulty_score: "float | None" = None
+    validation_passed: "bool | None" = None
+    validation_errors: int = 0
+    validation_warnings: int = 0
+    # Populated on success -- feeds dataset_summary.csv (see DatasetGenerator.generate()).
+    case_folder: "str | None" = None
+    template_id: "str | None" = None
+    template_kind: "str | None" = None
+    case_type: "str | None" = None
+    verdict: "str | None" = None
+    reject_code: "str | None" = None
+    signer_name: "str | None" = None
+
+
+class GeneratorContext:
+    """Holds every stateless-between-calls engine instance needed to generate a sample."""
+
+    def __init__(self, config: PipelineConfig) -> None:
+        self.config = config
+        self.template_provider = self._build_template_provider(config)
+        self.signature_dataset = SignatureDataset(
+            config.paths.dataset_dir,
+            signet_dir=config.paths.signet_dir,
+            gpds_dir=config.paths.gpds_dir,
+            bhsig260_hindi_dir=config.paths.bhsig260_hindi_dir,
+            bhsig260_bengali_dir=config.paths.bhsig260_bengali_dir,
+        )
+        self.preprocessor = SignaturePreprocessor()
+        self.placement_engine = PlacementEngine(config.placement)
+        self._placement_engine_procedural = self._build_procedural_placement_engine(config.placement)
+        self.augmentation_engine = AugmentationEngine(config.augmentation)
+        self.renderer = Renderer(config.rendering)
+        self.geometry_analyzer = GeometryAnalyzer()
+        self.difficulty_analyzer = DifficultyAnalyzer(config.difficulty)
+        self._difficulty_analyzer_procedural = self._build_procedural_difficulty_analyzer(config.difficulty)
+        self.metadata_exporter = MetadataExporter()
+        self.validation_engine = ValidationEngine(config.validation, config.difficulty)
+
+        # Trigger discovery eagerly so the first sample doesn't pay (and doesn't
+        # race another worker on) the filesystem-scan + annotation-cache cost.
+        self.template_provider.discover()
+        self.signature_dataset.discover()
+        self.identity_registry = SignerIdentityRegistry(self.template_provider, self.signature_dataset, config.seed)
+
+        # Set by _run_generic_sample/_run_case_sample on every call: the
+        # document right after signature placement, before document-level
+        # augmentation. run_sample()'s return signature is unchanged (still
+        # just metadata/image/report, matching every existing caller), so
+        # this is how the "before" image for save_before_after_preview
+        # escapes to _generate_and_export without a breaking API change.
+        # One GeneratorContext is used sequentially within a single worker
+        # process/sample at a time (see DatasetGenerator/_init_worker), so
+        # this is safe -- it always reflects the most recent run_sample()
+        # call by the time that call returns.
+        self.last_clean_image: "np.ndarray | None" = None
+
+        # Set alongside last_clean_image, same call sites, same lifetime
+        # caveat -- lets a caller (e.g. streamlit_app.py's interactive
+        # degradation lever) re-run GeometryAnalyzer/DifficultyAnalyzer on a
+        # hypothetically-more-or-less-degraded version of last_clean_image
+        # and get a live difficulty score, the same way the real
+        # generation pipeline would have scored it.
+        self.last_baseline_image: "np.ndarray | None" = None
+        self.last_target_box: "tuple[float, float, float, float] | None" = None
+        self.last_canvas_size: "tuple[int, int] | None" = None
+
+    @staticmethod
+    def _build_procedural_difficulty_analyzer(base_weights: DifficultyWeights) -> DifficultyAnalyzer:
+        """A second :class:`DifficultyAnalyzer`, sharing every field of
+        ``base_weights`` except the 3 smallness/faintness reference
+        constants -- used only for procedural-template samples (see
+        :meth:`_difficulty_analyzer_for`). If the caller already explicitly
+        set any of those 3 fields (a CLI ``--set difficulty....=`` override,
+        or a whole config profile like ``configs/procedural_templates.yaml``
+        that sets them itself), that choice is respected as-is and applied
+        to every sample regardless of template kind -- explicit
+        configuration always wins over this automatic per-kind default.
+        """
+        explicitly_set = base_weights.model_fields_set
+        overrides = {
+            key: value for key, value in _PROCEDURAL_DIFFICULTY_RECALIBRATION.items() if key not in explicitly_set
+        }
+        if not overrides:
+            return DifficultyAnalyzer(base_weights)
+        return DifficultyAnalyzer(base_weights.model_copy(update=overrides))
+
+    def _difficulty_analyzer_for(self, template_id: str) -> DifficultyAnalyzer:
+        """Picks the deck-literal or procedural-recalibrated analyzer for
+        this sample's template -- keyed per-sample (not per-run), so a
+        ``template_provider: both`` batch scores each sample against the
+        constants that actually fit its template family, instead of one
+        global setting silently misgrading half the batch."""
+        if template_kind(template_id) == "procedural":
+            return self._difficulty_analyzer_procedural
+        return self.difficulty_analyzer
+
+    @staticmethod
+    def _build_procedural_placement_engine(base_placement: PlacementConfig) -> PlacementEngine:
+        """A second :class:`PlacementEngine`, sharing every field of
+        ``base_placement`` except ``scale_factor`` -- used only for
+        procedural-template samples (see :meth:`_placement_engine_for`).
+
+        Deliberately does NOT use ``model_fields_set`` the way
+        :meth:`_build_procedural_difficulty_analyzer` does: unlike the
+        difficulty reference constants (never set by
+        ``configs/default.yaml``, so ``model_fields_set`` cleanly means
+        "a real user override"), ``configs/default.yaml`` itself always
+        sets ``placement.scale_factor`` (to its own natural-variation range,
+        ``{min: 0.78, max: 0.95}``) -- so ``model_fields_set`` would always
+        contain ``scale_factor`` on every run, silently defeating this
+        recalibration 100% of the time regardless of whether a real user
+        override exists. Compares the RESOLVED value against that known
+        shipped baseline instead: matches it -> safe to recalibrate;
+        differs (a real ``--set``/custom-profile override) -> respected
+        as-is for every template, same intent as the difficulty version.
+        """
+        if base_placement.scale_factor == _DEFAULT_SCALE_FACTOR:
+            return PlacementEngine(base_placement.model_copy(update=_PROCEDURAL_PLACEMENT_RECALIBRATION))
+        return PlacementEngine(base_placement)
+
+    def _placement_engine_for(self, template_id: str) -> PlacementEngine:
+        """Picks the base or procedural-recalibrated placement engine for
+        this sample's template -- same per-sample keying as
+        :meth:`_difficulty_analyzer_for`, for the same reason (a
+        ``template_provider: both`` batch shouldn't apply one family's
+        placement tuning to the other's templates)."""
+        if template_kind(template_id) == "procedural":
+            return self._placement_engine_procedural
+        return self.placement_engine
+
+    @staticmethod
+    def _build_template_provider(config: PipelineConfig) -> "TemplateProvider":
+        """Builds the configured template source(s) -- see ``paths.template_provider``
+        in ``src/config/schema.py``. ``TemplateLoader`` (scanned) and
+        ``ProceduralTemplateProvider`` are structurally interchangeable
+        (:class:`~src.templates.template_provider.TemplateProvider`); "both"
+        merges their id spaces via :class:`CompositeTemplateProvider`.
+        """
+        kind = config.paths.template_provider
+        excluded = frozenset(config.paths.excluded_template_ids)
+        if kind == "scanned":
+            return TemplateLoader(config.paths.image_dir, config.paths.annotations_dir, excluded_ids=excluded)
+        if kind == "procedural":
+            return ProceduralTemplateProvider(config.paths.cache_dir)
+        scanned = TemplateLoader(config.paths.image_dir, config.paths.annotations_dir, excluded_ids=excluded)
+        procedural = ProceduralTemplateProvider(config.paths.cache_dir)
+        return CompositeTemplateProvider([scanned, procedural])
+
+    def run_sample(
+        self, sample_index: int, seed_suffix: "str | None" = None,
+    ) -> tuple[dict[str, Any], "np.ndarray", "ValidationReport | None"]:
+        """Generate one sample.
+
+        Args:
+            sample_index: Used for both the output filename (``case_NNNNNN``)
+                and, when ``seed_suffix`` is ``None``, the seed itself.
+            seed_suffix: When given, the seed is derived from
+                ``f"{sample_index}_{seed_suffix}"`` instead of the bare
+                index -- a different, still-fully-deterministic draw for the
+                same nominal sample, used by
+                :meth:`run_sample_targeting_difficulty`'s retry loop so a
+                retry doesn't just reproduce the exact same result.
+        """
+        cfg = self.config
+        seed_key = sample_index if seed_suffix is None else f"{sample_index}_{seed_suffix}"
+        sm = SeedManager(cfg.seed, seed_key)
+        prefix = cfg.generation.filename_prefix
+        digits = cfg.generation.filename_digits
+        sample_id = f"{prefix}_{sample_index:0{digits}d}"
+
+        case_spec = CASE_TABLE.get(cfg.quality.case_type)
+        if case_spec is not None:
+            return self._run_case_sample(sample_id, sm, case_spec)
+        return self._run_generic_sample(sample_id, sm)
+
+    def run_sample_targeting_difficulty(
+        self, sample_index: int, target_score: float, tolerance: float = 0.05, max_attempts: int = 5,
+    ) -> tuple[dict[str, Any], "np.ndarray", "ValidationReport | None"]:
+        """Generate a sample whose ``difficulty.score`` lands within
+        ``tolerance`` of ``target_score``, retrying with fresh (but still
+        deterministic) randomness up to ``max_attempts`` times.
+
+        Ported from projectA's ``cli.py --difficulty {easy,medium,hard}``
+        retry-loop (``case_generation.py::generate_case_at_difficulty()``),
+        generalized from a coarse tier request to this pipeline's own
+        continuous ``[0, 1]`` score -- request ``0.55``, not ``"medium"``.
+        Every attempt is still fully reproducible from ``(seed,
+        sample_index, attempt)``; the closest-scoring attempt is kept even
+        if none lands within tolerance (metadata's ``difficulty_targeting``
+        block records whether it actually matched).
+        """
+        # Tracks last_clean_image alongside each attempt's result -- it's
+        # otherwise only ever set to the MOST RECENT run_sample() call
+        # (see the attribute's docstring in __init__), which would silently
+        # mismatch the returned metadata/image whenever the winning attempt
+        # (best-so-far, or the final match) isn't the last one tried.
+        best: "tuple[dict, np.ndarray, ValidationReport | None, float, np.ndarray | None, np.ndarray | None, tuple | None, tuple | None] | None" = None
+        for attempt in range(max_attempts):
+            suffix = None if attempt == 0 else f"difficulty_retry{attempt}"
+            metadata, image, report = self.run_sample(sample_index, seed_suffix=suffix)
+            score = metadata["difficulty"]["score"]
+            delta = abs(score - target_score)
+            if best is None or delta < best[3]:
+                best = (
+                    metadata, image, report, delta, self.last_clean_image,
+                    self.last_baseline_image, self.last_target_box, self.last_canvas_size,
+                )
+            if delta <= tolerance:
+                metadata["difficulty_targeting"] = {
+                    "requested_score": target_score, "tolerance": tolerance,
+                    "attempts_used": attempt + 1, "matched": True,
+                }
+                return metadata, image, report
+
+        metadata, image, report, delta, clean_image, baseline_image, target_box, canvas_size = best
+        self.last_clean_image = clean_image
+        self.last_baseline_image = baseline_image
+        self.last_target_box = target_box
+        self.last_canvas_size = canvas_size
+        metadata["difficulty_targeting"] = {
+            "requested_score": target_score, "tolerance": tolerance,
+            "attempts_used": max_attempts, "matched": False,
+        }
+        logger.warning(
+            "Sample %d: could not reach difficulty %.3f (+/-%.3f) in %d attempts; closest was %.3f (delta=%.3f).",
+            sample_index, target_score, tolerance, max_attempts, metadata["difficulty"]["score"], delta,
+        )
+        return metadata, image, report
+
+    def _run_generic_sample(self, sample_id: str, sm: SeedManager) -> tuple[dict[str, Any], "np.ndarray", "ValidationReport | None"]:
+        cfg = self.config
+        template = self._select_template(sm)
+        page_annotation = self._select_page(template)
+        box = self._select_box(page_annotation, sm)
+
+        document_image = load_image_rgb(template.image_path)
+        baseline_image = document_image  # pristine, pre-cosmetic-fields/pre-signature -- see analyze()'s baseline_image_rgb
+        canvas_h, canvas_w = document_image.shape[:2]
+        target_box = box.as_tuple()
+
+        signer_name = cfg.signer.name or generate_name(sm.python_rng("signer_name"))
+        signer_role = cfg.signer.role or box.role or generate_role(sm.python_rng("signer_role"))
+
+        document_image, cosmetic_field_boxes = self._maybe_fill_cosmetic_fields(document_image, template, page_annotation, box.id, signer_name, sm)
+
+        signature_record = self._select_signature(sm)
+        raw_signature = load_image_any(signature_record.path)
+        ink_color = INK_COLOR_POOL[int(sm.numpy_rng("ink_color").integers(0, len(INK_COLOR_POOL)))]
+        signature_rgba = self.preprocessor.process(raw_signature, ink_color=ink_color)
+
+        placement = self._placement_engine_for(template.template_id).place(
+            signature_rgba, target_box=target_box, canvas_size=(canvas_w, canvas_h), seed_manager=sm,
+        )
+        signature_layer, signature_aug_records = self.augmentation_engine.apply_to_signature(placement.layer, sm)
+        render_result = self.renderer.blend(document_image, signature_layer, sm)
+
+        # If this template has more than one signature box, fill every
+        # OTHER box with its own genuine signature too -- a "generic"
+        # (non-case-type) sample is meant to represent a normal, properly
+        # executed document, so leaving a second box conspicuously blank
+        # looked like a broken render rather than a deliberate test case.
+        # Only quality.case_type-driven samples (via _run_case_sample,
+        # e.g. 5a/5b) are meant to leave a box blank/wrong, each with its
+        # own labeled verdict/reject_code explaining why. These extra
+        # signatures aren't scored (geometry/difficulty stay keyed to the
+        # single primary `box` selected above, matching the rest of this
+        # method's schema), just rendered -- recorded under
+        # metadata["additional_signatures"] for traceability.
+        other_boxes = [b for b in page_annotation.signature_boxes if b.id != box.id]
+        additional_signatures: "list[dict] | None" = None
+        document_with_all_signatures = render_result.image
+        if other_boxes:
+            additional_signatures = []
+            for other_box in other_boxes:
+                sm_other = SeedManager(cfg.seed, f"{sm.sample_index}_box_{other_box.id}")
+                other_record = self._select_signature(sm_other)
+                other_raw = load_image_any(other_record.path)
+                other_ink_color = INK_COLOR_POOL[int(sm_other.numpy_rng("ink_color").integers(0, len(INK_COLOR_POOL)))]
+                other_rgba = self.preprocessor.process(other_raw, ink_color=other_ink_color)
+                other_placement = self._placement_engine_for(template.template_id).place(
+                    other_rgba, target_box=other_box.as_tuple(), canvas_size=(canvas_w, canvas_h), seed_manager=sm_other,
+                )
+                other_layer, _ = self.augmentation_engine.apply_to_signature(other_placement.layer, sm_other)
+                other_render = self.renderer.blend(document_with_all_signatures, other_layer, sm_other)
+                document_with_all_signatures = other_render.image
+                other_name = self.identity_registry.display_name(other_record.signer_group)
+                document_with_all_signatures, other_field_boxes = self._fill_box_cosmetic_field(
+                    document_with_all_signatures, template, page_annotation, other_box.id, other_name, sm_other,
+                )
+                if other_field_boxes:
+                    cosmetic_field_boxes = {**(cosmetic_field_boxes or {}), **other_field_boxes}
+                additional_signatures.append({
+                    "box_id": other_box.id, "role": other_box.role,
+                    "signature_id": other_record.signature_id, "signer_group": other_record.signer_group,
+                    "source_type": other_record.source_type, "script": other_record.script,
+                    "ref_path": other_record.path.as_posix(),
+                })
+
+        self.last_clean_image = document_with_all_signatures.copy()
+        self.last_baseline_image = baseline_image
+        self.last_target_box = target_box
+        self.last_canvas_size = (canvas_w, canvas_h)
+        final_image, document_aug_records, warped_baseline = self.augmentation_engine.apply_to_document(
+            document_with_all_signatures, sm, baseline_image=baseline_image,
+        )
+
+        geometry = self.geometry_analyzer.analyze(
+            final_image, target_box=target_box, canvas_size=(canvas_w, canvas_h),
+            placement_hint_bbox=placement.intended_bbox,
+            baseline_image_rgb=warped_baseline,
+        )
+        difficulty = self._difficulty_analyzer_for(template.template_id).analyze(geometry)
+
+        quality_rng = sm.numpy_rng("quality")
+        quality_values = {
+            "scanner_quality": round(resolve_scalar_or_range(cfg.quality.scanner_quality, quality_rng), 4),
+            "paper_quality": round(resolve_scalar_or_range(cfg.quality.paper_quality, quality_rng), 4),
+            "render_quality": round(resolve_scalar_or_range(cfg.quality.render_quality, quality_rng), 4),
+        }
+
+        metadata = self.metadata_exporter.build(
+            document_id=sample_id,
+            template_id=template.template_id,
+            document_type=template.annotation.document_type,
+            page=page_annotation.page,
+            page_size=(canvas_w, canvas_h),
+            dpi=page_annotation.dpi,
+            signer_name=signer_name,
+            signer_role=signer_role,
+            signature_source=signature_record,
+            placement=placement,
+            render=render_result,
+            signature_aug=signature_aug_records,
+            document_aug=document_aug_records,
+            geometry=geometry,
+            difficulty=difficulty,
+            quality_cfg=cfg.quality,
+            quality_values=quality_values,
+            seed=cfg.seed,
+            sample_index=sm.sample_index,
+            verification=None,
+        )
+        if cosmetic_field_boxes:
+            metadata["cosmetics"] = {"field_boxes": cosmetic_field_boxes}
+        if additional_signatures:
+            metadata["additional_signatures"] = additional_signatures
+
+        validation_report = None
+        if cfg.generation.run_validation:
+            validation_report = self.validation_engine.validate(metadata)
+
+        return metadata, final_image, validation_report
+
+    # ------------------------------------------------------------------ #
+    # Verification test-case generation (case_type-driven)
+    # ------------------------------------------------------------------ #
+
+    def _run_case_sample(
+        self, sample_id: str, sm: SeedManager, case_spec: CaseSpec,
+    ) -> tuple[dict[str, Any], "np.ndarray", "ValidationReport | None"]:
+        cfg = self.config
+        template = self._select_template_for_case(sm, case_spec)
+        page_annotation = self._select_page(template)
+        boxes = page_annotation.signature_boxes
+        if not boxes:
+            raise ValueError(f"Page {page_annotation.page} has no signature boxes defined.")
+        target_box, companion_box = self._select_case_boxes(boxes, sm)
+
+        document_image = load_image_rgb(template.image_path)
+        baseline_image = document_image  # pristine, pre-cosmetic-fields/pre-signature -- see analyze()'s baseline_image_rgb
+        canvas_h, canvas_w = document_image.shape[:2]
+        target_box_tuple = target_box.as_tuple()
+        canvas_size = (canvas_w, canvas_h)
+
+        expected_group = self.identity_registry.authorized_signer_group(template.template_id, target_box.id)
+        signer_name = cfg.signer.name or self.identity_registry.display_name(expected_group)
+        signer_role = cfg.signer.role or target_box.role
+
+        document_image, cosmetic_field_boxes = self._maybe_fill_cosmetic_fields(document_image, template, page_annotation, target_box.id, signer_name, sm)
+
+        ink_color = INK_COLOR_POOL[int(sm.numpy_rng("ink_color").integers(0, len(INK_COLOR_POOL)))]
+
+        document_after_companion = document_image
+        companion_info: "dict[str, Any] | None" = None
+        additional_signatures: "list[dict] | None" = None
+        if case_spec.fill_companion:
+            if companion_box is None:
+                logger.warning(
+                    "Case %s wants a companion signer but template %s has only one signature box; skipping.",
+                    case_spec.code, template.template_id,
+                )
+            else:
+                companion_group = self.identity_registry.authorized_signer_group(template.template_id, companion_box.id)
+                sm_companion = SeedManager(cfg.seed, f"{sm.sample_index}_companion")
+                companion_record = self.signature_dataset.sample_from_group(
+                    sm_companion.numpy_rng("case:companion_signature"), companion_group, source_filter="genuine",
+                )
+                companion_raw = load_image_any(companion_record.path)
+                companion_rgba = self.preprocessor.process(companion_raw, ink_color=ink_color)
+                companion_placement = self._placement_engine_for(template.template_id).place(
+                    companion_rgba, target_box=companion_box.as_tuple(), canvas_size=canvas_size, seed_manager=sm_companion,
+                )
+                companion_layer, _ = self.augmentation_engine.apply_to_signature(companion_placement.layer, sm_companion)
+                companion_render = self.renderer.blend(document_after_companion, companion_layer, sm_companion)
+                document_after_companion = companion_render.image
+                companion_name = self.identity_registry.display_name(companion_group)
+                document_after_companion, companion_field_boxes = self._fill_box_cosmetic_field(
+                    document_after_companion, template, page_annotation, companion_box.id, companion_name, sm_companion,
+                )
+                if companion_field_boxes:
+                    cosmetic_field_boxes = {**(cosmetic_field_boxes or {}), **companion_field_boxes}
+                companion_info = {
+                    "box_id": companion_box.id, "filled": True,
+                    "signer_group": companion_group, "script": self.identity_registry.script_for(companion_group),
+                    "matches_expected": True, "ref_path": str(companion_record.path),
+                }
+        elif companion_box is not None:
+            # This case type doesn't officially test a second signer (e.g.
+            # 1a/1b/1d/2a/2b/3a/3b -- fill_companion=False), but the
+            # template it landed on still has a second signature box. Fill
+            # it correctly anyway, same reasoning as the generic-sample
+            # path (_run_generic_sample): a properly executed document
+            # shouldn't have a conspicuously blank second box with no
+            # verdict/reject_code explaining why -- only fill_companion=True
+            # types (5a/5b) are meant to leave a required box blank/wrong,
+            # each with its own labeled verdict. Tracked separately from
+            # `companion_info`/`verification.companion` (which describes
+            # this case type's OWN tested companion behavior) under the
+            # same "additional_signatures" key the generic path uses.
+            extra_group = self.identity_registry.authorized_signer_group(template.template_id, companion_box.id)
+            sm_extra = SeedManager(cfg.seed, f"{sm.sample_index}_extra_{companion_box.id}")
+            extra_record = self.signature_dataset.sample_from_group(
+                sm_extra.numpy_rng("case:extra_signature"), extra_group, source_filter="genuine",
+            )
+            extra_raw = load_image_any(extra_record.path)
+            extra_rgba = self.preprocessor.process(extra_raw, ink_color=ink_color)
+            extra_placement = self._placement_engine_for(template.template_id).place(
+                extra_rgba, target_box=companion_box.as_tuple(), canvas_size=canvas_size, seed_manager=sm_extra,
+            )
+            extra_layer, _ = self.augmentation_engine.apply_to_signature(extra_placement.layer, sm_extra)
+            extra_render = self.renderer.blend(document_after_companion, extra_layer, sm_extra)
+            document_after_companion = extra_render.image
+            extra_name = self.identity_registry.display_name(extra_group)
+            document_after_companion, extra_field_boxes = self._fill_box_cosmetic_field(
+                document_after_companion, template, page_annotation, companion_box.id, extra_name, sm_extra,
+            )
+            if extra_field_boxes:
+                cosmetic_field_boxes = {**(cosmetic_field_boxes or {}), **extra_field_boxes}
+            additional_signatures = [{
+                "box_id": companion_box.id, "role": companion_box.role,
+                "signature_id": extra_record.signature_id, "signer_group": extra_group,
+                "source_type": extra_record.source_type, "script": self.identity_registry.script_for(extra_group),
+                "ref_path": extra_record.path.as_posix(),
+            }]
+
+        actual_record, actual_group, actual_source_type, target_rgba = self._resolve_case_signature(
+            case_spec, template, target_box, companion_box, expected_group, signer_name, ink_color, sm,
+        )
+
+        if target_rgba is None:
+            placement = PlacementResult(
+                layer=np.zeros((canvas_h, canvas_w, 4), dtype=np.uint8),
+                target_box=target_box_tuple, intended_bbox=target_box_tuple,
+                x_offset=0.0, y_offset=0.0, rotation_deg=0.0, scale_factor=0.0,
+                anchor_point=cfg.placement.anchor_point, misplaced=False,
+                misplacement_dx=0.0, misplacement_dy=0.0,
+            )
+        else:
+            placement = self._placement_engine_for(template.template_id).place(
+                target_rgba, target_box=target_box_tuple, canvas_size=canvas_size, seed_manager=sm,
+            )
+        signature_layer, signature_aug_records = self.augmentation_engine.apply_to_signature(placement.layer, sm)
+        render_result = self.renderer.blend(document_after_companion, signature_layer, sm)
+        self.last_clean_image = render_result.image.copy()
+        self.last_baseline_image = baseline_image
+        self.last_target_box = target_box_tuple
+        self.last_canvas_size = canvas_size
+        final_image, document_aug_records, warped_baseline = self.augmentation_engine.apply_to_document(
+            render_result.image, sm, baseline_image=baseline_image,
+        )
+
+        geometry = self.geometry_analyzer.analyze(
+            final_image, target_box=target_box_tuple, canvas_size=canvas_size,
+            placement_hint_bbox=placement.intended_bbox,
+            baseline_image_rgb=warped_baseline,
+            expect_blank=target_rgba is None,
+        )
+        difficulty = self._difficulty_analyzer_for(template.template_id).analyze(geometry)
+
+        quality_rng = sm.numpy_rng("quality")
+        quality_values = {
+            "scanner_quality": round(resolve_scalar_or_range(cfg.quality.scanner_quality, quality_rng), 4),
+            "paper_quality": round(resolve_scalar_or_range(cfg.quality.paper_quality, quality_rng), 4),
+            "render_quality": round(resolve_scalar_or_range(cfg.quality.render_quality, quality_rng), 4),
+        }
+
+        verification_record = {
+            "case_type": case_spec.code,
+            "verdict": case_spec.verdict,
+            "reject_code": case_spec.reject_code,
+            "target_box_id": target_box.id,
+            "expected_signer_group": expected_group,
+            "expected_signer_script": self.identity_registry.script_for(expected_group),
+            "actual_signer_group": actual_group,
+            "actual_source_type": actual_source_type,
+            "actual_signer_script": self.identity_registry.script_for(actual_group) if actual_group else None,
+            "companion": companion_info,
+        }
+
+        metadata = self.metadata_exporter.build(
+            document_id=sample_id,
+            template_id=template.template_id,
+            document_type=template.annotation.document_type,
+            page=page_annotation.page,
+            page_size=(canvas_w, canvas_h),
+            dpi=page_annotation.dpi,
+            signer_name=signer_name,
+            signer_role=signer_role,
+            signature_source=actual_record,
+            placement=placement,
+            render=render_result,
+            signature_aug=signature_aug_records,
+            document_aug=document_aug_records,
+            geometry=geometry,
+            difficulty=difficulty,
+            quality_cfg=cfg.quality,
+            quality_values=quality_values,
+            seed=cfg.seed,
+            sample_index=sm.sample_index,
+            verification=verification_record,
+        )
+        if cosmetic_field_boxes:
+            metadata["cosmetics"] = {"field_boxes": cosmetic_field_boxes}
+        if additional_signatures:
+            metadata["additional_signatures"] = additional_signatures
+
+        validation_report = None
+        if cfg.generation.run_validation:
+            validation_report = self.validation_engine.validate(metadata)
+
+        return metadata, final_image, validation_report
+
+    def _maybe_fill_cosmetic_fields(
+        self, document_image: "np.ndarray", template: Template, page_annotation: TemplatePageAnnotation,
+        primary_box_id: str, signer_name: str, sm: SeedManager,
+    ) -> "tuple[np.ndarray, dict | None]":
+        """Fills non-scored cosmetic fields if enabled -- SWIFT/NRIC/UEN/amounts
+        on procedural templates (full field set, exact coordinates known from
+        the drawing spec -- see :mod:`src.cosmetics.cosmetic_fields`), or a
+        pre-measured name/company-name on scanned templates (coordinates
+        cached in the template's own annotation file -- see
+        :mod:`src.cosmetics.scanned_cosmetic_fields` and
+        ``scripts/detect_cosmetic_field_coordinates.py`` for how they were
+        measured -- and the 2 of 9 scanned templates that are stock photos
+        with no genuine blank field to fill, which just have no
+        ``cosmetic_fields`` entries). No-op (image unchanged, ``None``
+        boxes) when disabled, or when a scanned template has none configured.
+
+        Only fills fields with no ``box_id`` (entity-level, e.g.
+        ``company_name``) or tied to ``primary_box_id`` -- on a
+        multi-signature-box template, a field belonging to a DIFFERENT box
+        (e.g. a companion signatory's own "Print Name:" line) is filled
+        separately once that signer's identity is known, by
+        :meth:`_fill_box_cosmetic_field`. This method runs early, before
+        any companion/additional signer is resolved, so it can only ever
+        speak for the box it's actually given.
+        """
+        if not self.config.document.enable_cosmetic_fields:
+            return document_image, None
+
+        if template_kind(template.template_id) == "scanned":
+            fields = [f for f in page_annotation.cosmetic_fields if f.box_id in (None, primary_box_id)]
+            image, boxes = fill_scanned_cosmetic_fields(
+                document_image, fields, signer_name, sm.python_rng("cosmetic_fields"),
+            )
+            return image, (boxes or None)
+
+        field_boxes_fn = getattr(self.template_provider, "field_boxes_for", None)
+        if field_boxes_fn is None:
+            return document_image, None
+        template_cfg = field_boxes_fn(template.template_id)
+        if template_cfg is None:
+            return document_image, None
+        return fill_all_cosmetic_fields(document_image, template_cfg, signer_name, sm.python_rng("cosmetic_fields"))
+
+    def _fill_box_cosmetic_field(
+        self, document_image: "np.ndarray", template: Template, page_annotation: TemplatePageAnnotation,
+        box_id: str, name: str, sm: SeedManager,
+    ) -> "tuple[np.ndarray, dict | None]":
+        """Second, later pass for a companion/additional signature box's own
+        cosmetic field (e.g. bank_doc_04's LENDER "Print Name:" line,
+        distinct from the BORROWER field :meth:`_maybe_fill_cosmetic_fields`
+        already handled) -- called once that box's actual signer identity
+        is known, which is always *after* the first pass already ran (see
+        ``_run_case_sample``/``_run_generic_sample``). No-op if cosmetic
+        fields are disabled, the template is procedural (no box-tied
+        fields exist there), or this particular box has no such field
+        (true for most single-name-field scanned templates).
+        """
+        if not self.config.document.enable_cosmetic_fields or template_kind(template.template_id) != "scanned":
+            return document_image, None
+        fields = [f for f in page_annotation.cosmetic_fields if f.box_id == box_id]
+        if not fields:
+            return document_image, None
+        image, boxes = fill_scanned_cosmetic_fields(document_image, fields, name, sm.python_rng("cosmetic_fields"))
+        return image, (boxes or None)
+
+    def _select_template_for_case(self, sm: SeedManager, case_spec: CaseSpec) -> Template:
+        cfg = self.config
+        if cfg.document.template:
+            return self.template_provider.get(cfg.document.template)
+
+        templates = self.template_provider.discover()
+        if case_spec.fill_companion:
+            candidates = sorted(
+                tid for tid, t in templates.items() if len(t.annotation.pages[0].signature_boxes) >= 2
+            )
+            if not candidates:
+                logger.warning(
+                    "Case %s needs a multi-box template but none were found; using the full template pool.",
+                    case_spec.code,
+                )
+                candidates = sorted(templates.keys())
+        else:
+            candidates = sorted(templates.keys())
+
+        idx = int(sm.numpy_rng("template_selection").integers(0, len(candidates)))
+        return self.template_provider.get(candidates[idx])
+
+    def _select_case_boxes(
+        self, boxes: "list[SignatureBoxAnnotation]", sm: SeedManager,
+    ) -> "tuple[SignatureBoxAnnotation, SignatureBoxAnnotation | None]":
+        idx = int(sm.numpy_rng("box_selection").integers(0, len(boxes)))
+        target = boxes[idx]
+        others = [b for i, b in enumerate(boxes) if i != idx]
+        companion = others[0] if others else None
+        return target, companion
+
+    def _resolve_case_signature(
+        self, case_spec: CaseSpec, template: Template, target_box: "SignatureBoxAnnotation",
+        companion_box: "SignatureBoxAnnotation | None", expected_group: str, expected_name: str,
+        ink_color: tuple[int, int, int], sm: SeedManager,
+    ) -> "tuple[SignatureRecord | None, str | None, str, np.ndarray | None]":
+        """Resolve what actually goes in the target box for one case type.
+
+        Returns ``(signature_record_or_none, actual_signer_group_or_none,
+        actual_source_type, rgba_layer_or_none)``.
+        """
+        mode = case_spec.signature_mode
+
+        if mode == "blank":
+            return None, None, "none", None
+
+        if mode == "printed_text":
+            layer_rgba = render_printed_name_stamp(expected_name, ink_color=ink_color)
+            return None, None, "printed", layer_rgba
+
+        if mode == "genuine_correct":
+            group, source_filter, stage = expected_group, "genuine", "case:target_signature"
+        elif mode == "genuine_variation":
+            group, source_filter, stage = expected_group, "genuine", "case:target_signature_variant"
+        elif mode == "forged_skilled":
+            group, source_filter, stage = expected_group, "forged", "case:forged_signature"
+        elif mode == "wrong_signer_genuine":
+            wrong_group = self.identity_registry.random_wrong_signer_group(
+                sm.python_rng("case:wrong_signer"), expected_group,
+            )
+            group, source_filter, stage = wrong_group, "genuine", "case:wrong_signature"
+        elif mode == "role_swap":
+            if companion_box is None:
+                raise ValueError(f"Case {case_spec.code} requires a companion box but none is available.")
+            group = self.identity_registry.authorized_signer_group(template.template_id, companion_box.id)
+            source_filter, stage = "genuine", "case:role_swap_signature"
+        elif mode == "foreign_template_signer":
+            group, _foreign_template_id, _foreign_box_id = self.identity_registry.foreign_authorized(template.template_id)
+            source_filter, stage = "genuine", "case:foreign_signature"
+        else:
+            raise ValueError(f"Unknown signature_mode: {mode!r}")
+
+        record = self.signature_dataset.sample_from_group(sm.numpy_rng(stage), group, source_filter)
+        raw = load_image_any(record.path)
+        layer_rgba = self.preprocessor.process(raw, ink_color=ink_color)
+        return record, group, record.source_type, layer_rgba
+
+    def _select_template(self, sm: SeedManager):
+        cfg = self.config
+        if cfg.document.template:
+            return self.template_provider.get(cfg.document.template)
+        return self.template_provider.random_template(sm.numpy_rng("template_selection"))
+
+    def _select_page(self, template):
+        cfg = self.config
+        if cfg.document.page is not None:
+            return template.annotation.page_by_number(cfg.document.page)
+        return template.annotation.pages[0]
+
+    def _select_box(self, page_annotation, sm: SeedManager):
+        boxes = page_annotation.signature_boxes
+        if not boxes:
+            raise ValueError(f"Page {page_annotation.page} has no signature boxes defined.")
+        idx = int(sm.numpy_rng("box_selection").integers(0, len(boxes)))
+        return boxes[idx]
+
+    def _select_signature(self, sm: SeedManager):
+        cfg = self.config
+        if cfg.signature_selection.signature_id:
+            return self.signature_dataset.get(cfg.signature_selection.signature_id)
+        return self.signature_dataset.sample(
+            sm.numpy_rng("signature_selection"), source_filter=cfg.signature_selection.source_filter,
+        )
+
+
+_WORKER_CONTEXT: "GeneratorContext | None" = None
+_WORKER_CONFIG: "PipelineConfig | None" = None
+
+
+def _init_worker(config: PipelineConfig) -> None:
+    global _WORKER_CONTEXT, _WORKER_CONFIG
+    _WORKER_CONFIG = config
+    _WORKER_CONTEXT = GeneratorContext(config)
+
+
+def compute_extra_signature_overlays(
+    ctx: "GeneratorContext", metadata: dict, final_image: "np.ndarray",
+) -> "list[dict]":
+    """Re-runs :class:`~src.analysis.geometry_analyzer.GeometryAnalyzer` on
+    every OTHER signed box in this sample (companion + additional_signatures)
+    -- purely for the before/after metrics-preview overlay, never for
+    scoring (geometry/difficulty stay keyed to the one target box the rest
+    of the pipeline scores). Without this, a preview of a multi-box
+    template shows one fully-explained scored signature and one
+    unexplained, unlabeled scribble elsewhere on the page. See
+    :func:`src.visualization.before_after_preview.save_before_after_preview`'s
+    ``extra_boxes`` parameter.
+    """
+    template = ctx.template_provider.get(metadata["template"])
+    boxes_by_id = {b.id: b for b in template.annotation.pages[0].signature_boxes}
+    canvas_size = (metadata["page_size"][0], metadata["page_size"][1])
+
+    effects = (metadata.get("augmentation") or {}).get("effects") or {}
+    geometric_applied = any((effects.get(name) or {}).get("applied") for name in _GEOMETRIC_DOCUMENT_EFFECTS)
+    baseline = None if geometric_applied else load_image_rgb(template.image_path)
+
+    entries: "list[tuple[str, str]]" = []
+    companion = (metadata.get("verification") or {}).get("companion")
+    if companion and companion.get("filled") and companion.get("box_id") in boxes_by_id:
+        entries.append((companion["box_id"], f"{boxes_by_id[companion['box_id']].role} (companion)"))
+    for extra in metadata.get("additional_signatures") or []:
+        if extra["box_id"] in boxes_by_id and extra["box_id"] not in {e[0] for e in entries}:
+            entries.append((extra["box_id"], f"{extra.get('role') or extra['box_id']} (other box)"))
+
+    overlays = []
+    for box_id, label in entries:
+        box = boxes_by_id[box_id]
+        result = ctx.geometry_analyzer.analyze(
+            final_image, target_box=box.as_tuple(), canvas_size=canvas_size, baseline_image_rgb=baseline,
+        )
+        overlays.append({
+            "label": label,
+            "geometry": {
+                "target_box": list(result.target_box), "actual_box": list(result.actual_box),
+                "signature_polygon_px": result.signature_polygon_px,
+                "display_contours": result.display_contours,
+            },
+            "metrics": {
+                "box_coverage_pct": result.box_coverage_pct, "containment_pct": result.containment_pct,
+                "ink_fill_ratio": result.ink_fill_ratio, "polygon_overlap_proxy": result.polygon_overlap_proxy,
+                "mean_dark_intensity": result.mean_dark_intensity, "margin_utilization": result.margin_utilization,
+            } if result.found else None,
+        })
+    return overlays
+
+
+def _case_folder_name(sample_id: str, case_type: "str | None") -> str:
+    """Every sample gets its own folder (ported from projectA's per-case-folder
+    convention -- see the module docstring's "Output layout" note) rather than
+    scattering flat sample_NNNNNN.png/.json pairs across output_dir. The
+    template kind (scanned/procedural) isn't knowable until after the sample
+    is actually generated (template selection is random per sample unless
+    `document.template` pins it), so it can't be part of a name computed
+    up-front for the overwrite-skip check below -- it's still recorded
+    prominently in the ground-truth JSON's `template_kind` field and in
+    dataset_summary.csv instead.
+    """
+    return f"{sample_id}_{case_type}" if case_type else sample_id
+
+
+def _export_reference_signature(src_path: str, dst_path: Path) -> None:
+    """Re-encode a reference signature image as a REAL PNG, regardless of
+    its source format. BHSig260 ships ``.tif``; CEDAR/SigNet are already
+    ``.png``. A previous version of this just ``shutil.copy2``'d the raw
+    bytes under a ``*_reference_signature.png`` name -- for BHSig260-sourced
+    signatures that left actual TIFF bytes inside a file misleadingly named
+    ``.png``, which any standards-compliant image viewer correctly refuses
+    to open ("error occurred while loading image"). Decoding with Pillow
+    and re-saving guarantees the extension always matches the real content.
+    """
+    with Image.open(src_path) as im:
+        im.convert("RGB").save(dst_path)
+
+
+def _generate_and_export(sample_index: int) -> SampleOutcome:
+    """Module-level worker entry point: generate one sample and write it to disk."""
+    assert _WORKER_CONTEXT is not None and _WORKER_CONFIG is not None, "Worker not initialized"
+    cfg = _WORKER_CONFIG
+    ctx = _WORKER_CONTEXT
+    prefix = cfg.generation.filename_prefix
+    digits = cfg.generation.filename_digits
+    sample_id = f"{prefix}_{sample_index:0{digits}d}"
+    output_dir = Path(cfg.paths.output_dir)
+    case_folder = _case_folder_name(sample_id, cfg.quality.case_type)
+    case_dir = output_dir / case_folder
+    png_path = case_dir / f"{sample_id}_document.png"
+    json_path = case_dir / f"{sample_id}_ground_truth.json"
+
+    if not cfg.generation.overwrite and png_path.exists() and json_path.exists():
+        return SampleOutcome(sample_id=sample_id, success=True, error="skipped_existing", case_folder=case_folder)
+
+    try:
+        if cfg.generation.target_difficulty is not None:
+            metadata, final_image, validation_report = ctx.run_sample_targeting_difficulty(
+                sample_index, cfg.generation.target_difficulty,
+                cfg.generation.difficulty_tolerance, cfg.generation.difficulty_max_attempts,
+            )
+        else:
+            metadata, final_image, validation_report = ctx.run_sample(sample_index)
+        case_dir.mkdir(parents=True, exist_ok=True)
+        save_image_rgb(png_path, final_image)
+
+        # Copy the actual reference signature image(s) used INTO the case
+        # folder (not just a path pointing back at the raw dataset) --
+        # ported from projectA's case_generation.py::save_case(), so each
+        # case folder is a fully self-contained, portable unit: open it,
+        # see the rendered document, the exact reference signature(s) it
+        # was compared against, and the ground truth, with no dependency on
+        # the raw dataset being present. `None`/missing for signature_modes
+        # with no real ink (blank box, printed-text-only).
+        sig_source = metadata.get("signature_source") or {}
+        if sig_source.get("path"):
+            try:
+                _export_reference_signature(sig_source["path"], case_dir / f"{sample_id}_reference_signature.png")
+            except OSError:
+                logger.warning("Sample %s: could not copy reference signature from %s", sample_id, sig_source["path"])
+        companion = (metadata.get("verification") or {}).get("companion") or {}
+        if companion.get("ref_path"):
+            try:
+                _export_reference_signature(companion["ref_path"], case_dir / f"{sample_id}_companion_signature.png")
+            except OSError:
+                logger.warning("Sample %s: could not copy companion signature from %s", sample_id, companion["ref_path"])
+        for extra in metadata.get("additional_signatures") or []:
+            try:
+                _export_reference_signature(extra["ref_path"], case_dir / f"{sample_id}_{extra['box_id']}_signature.png")
+            except OSError:
+                logger.warning("Sample %s: could not copy additional signature from %s", sample_id, extra["ref_path"])
+
+        ctx.metadata_exporter.export(metadata, json_path)
+
+        if cfg.generation.save_visualization:
+            viz_path = case_dir / f"{sample_id}_visualization.png"
+            save_visualization(viz_path, final_image, metadata)
+
+        if cfg.generation.save_before_after_preview and ctx.last_clean_image is not None:
+            # Grouped in one shared folder rather than scattered one-per-case --
+            # easier to flip through every sample's metrics preview in a row
+            # without opening each case folder individually.
+            before_after_path = output_dir / "before_after" / f"{sample_id}_before_after.png"
+            extra_boxes = compute_extra_signature_overlays(ctx, metadata, final_image)
+            save_before_after_preview(before_after_path, ctx.last_clean_image, final_image, metadata, cfg.difficulty, extra_boxes)
+
+        verification = metadata.get("verification") or {}
+        outcome = SampleOutcome(
+            sample_id=sample_id, success=True,
+            difficulty_tier=metadata["difficulty"]["tier"],
+            difficulty_score=metadata["difficulty"]["score"],
+            case_folder=case_folder,
+            template_id=metadata["template"],
+            template_kind=metadata["template_kind"],
+            case_type=verification.get("case_type"),
+            verdict=verification.get("verdict"),
+            reject_code=verification.get("reject_code"),
+            signer_name=metadata["signer"]["name"],
+        )
+        if validation_report is not None:
+            outcome.validation_passed = validation_report.passed
+            outcome.validation_errors = len(validation_report.errors)
+            outcome.validation_warnings = len(validation_report.warnings)
+            if not validation_report.passed:
+                logger.warning(
+                    "Sample %s failed validation: %s", sample_id,
+                    "; ".join(i.message for i in validation_report.errors),
+                )
+        return outcome
+    except Exception as exc:  # noqa: BLE001 - report and continue the batch
+        logger.exception("Failed to generate sample %s", sample_id)
+        return SampleOutcome(sample_id=sample_id, success=False, error=str(exc), case_folder=case_folder)
+
+
+@dataclass
+class GenerationSummary:
+    """Batch-level summary returned by :meth:`DatasetGenerator.generate`."""
+
+    total: int
+    succeeded: int
+    failed: int
+    skipped_existing: int
+    validation_passed: int
+    validation_failed: int
+    tier_counts: dict[str, int]
+    failed_samples: list[str]
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "total": self.total,
+            "succeeded": self.succeeded,
+            "failed": self.failed,
+            "skipped_existing": self.skipped_existing,
+            "validation_passed": self.validation_passed,
+            "validation_failed": self.validation_failed,
+            "tier_counts": self.tier_counts,
+            "failed_samples": self.failed_samples,
+        }
+
+
+class DatasetGenerator:
+    """Drives batch generation of a synthetic signed-document dataset.
+
+    Example:
+        >>> generator = DatasetGenerator(config)
+        >>> summary = generator.generate()
+    """
+
+    def __init__(self, config: PipelineConfig) -> None:
+        self.config = config
+        Path(config.paths.output_dir).mkdir(parents=True, exist_ok=True)
+
+    def generate(self) -> GenerationSummary:
+        """Generate ``config.generation.count`` samples, sequentially or via a worker pool."""
+        cfg = self.config
+        indices = list(range(cfg.generation.start_index, cfg.generation.start_index + cfg.generation.count))
+        outcomes: list[SampleOutcome] = []
+
+        if cfg.generation.workers <= 1:
+            _init_worker(cfg)
+            for idx in tqdm(indices, desc="Generating samples", unit="doc"):
+                outcomes.append(_generate_and_export(idx))
+        else:
+            with ProcessPoolExecutor(
+                max_workers=cfg.generation.workers, initializer=_init_worker, initargs=(cfg,),
+            ) as pool:
+                futures = {pool.submit(_generate_and_export, idx): idx for idx in indices}
+                for future in tqdm(as_completed(futures), total=len(futures), desc="Generating samples", unit="doc"):
+                    outcomes.append(future.result())
+
+        self._write_summary_csv(outcomes)
+        return self._summarize(outcomes)
+
+    def _write_summary_csv(self, outcomes: "list[SampleOutcome]") -> None:
+        """One row per sample -- a human-scannable overview of the whole batch
+        (case folder, which template/kind it landed on, verdict, difficulty)
+        without opening every ground_truth.json individually. Written
+        regardless of workers=1 vs multi-process, since it's built from the
+        already-collected SampleOutcome list, not during generation itself.
+        """
+        path = Path(self.config.paths.output_dir) / "dataset_summary.csv"
+        fieldnames = [
+            "sample_id", "case_folder", "success", "template_id", "template_kind",
+            "case_type", "verdict", "reject_code", "signer_name",
+            "difficulty_tier", "difficulty_score",
+        ]
+        with path.open("w", newline="", encoding="utf-8") as f:
+            writer = csv.DictWriter(f, fieldnames=fieldnames)
+            writer.writeheader()
+            for o in outcomes:
+                writer.writerow({
+                    "sample_id": o.sample_id, "case_folder": o.case_folder, "success": o.success,
+                    "template_id": o.template_id, "template_kind": o.template_kind,
+                    "case_type": o.case_type, "verdict": o.verdict, "reject_code": o.reject_code,
+                    "signer_name": o.signer_name,
+                    "difficulty_tier": o.difficulty_tier, "difficulty_score": o.difficulty_score,
+                })
+        logger.info("Wrote dataset summary to %s", path)
+
+    @staticmethod
+    def _summarize(outcomes: list[SampleOutcome]) -> GenerationSummary:
+        succeeded = [o for o in outcomes if o.success]
+        failed = [o for o in outcomes if not o.success]
+        skipped = [o for o in succeeded if o.error == "skipped_existing"]
+        validated = [o for o in succeeded if o.validation_passed is not None]
+        validation_passed = sum(1 for o in validated if o.validation_passed)
+        validation_failed = sum(1 for o in validated if not o.validation_passed)
+
+        tier_counts: dict[str, int] = {}
+        for o in succeeded:
+            if o.difficulty_tier:
+                tier_counts[o.difficulty_tier] = tier_counts.get(o.difficulty_tier, 0) + 1
+
+        summary = GenerationSummary(
+            total=len(outcomes),
+            succeeded=len(succeeded),
+            failed=len(failed),
+            skipped_existing=len(skipped),
+            validation_passed=validation_passed,
+            validation_failed=validation_failed,
+            tier_counts=tier_counts,
+            failed_samples=[o.sample_id for o in failed],
+        )
+        logger.info("Generation summary: %s", summary.as_dict())
+        return summary
diff --git a/unified_pipeline/src/metadata/__init__.py b/unified_pipeline/src/metadata/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4649838c4977c52e729c858949c582fe71e92605
--- /dev/null
+++ b/unified_pipeline/src/metadata/__init__.py
@@ -0,0 +1 @@
+"""Metadata assembly and export."""
diff --git a/unified_pipeline/src/metadata/metadata_exporter.py b/unified_pipeline/src/metadata/metadata_exporter.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a96d149a48003ae321cd730080b6e2cb3b391e5
--- /dev/null
+++ b/unified_pipeline/src/metadata/metadata_exporter.py
@@ -0,0 +1,257 @@
+"""Assembles and writes the complete per-sample metadata JSON.
+
+The exported schema always includes every documented field -- augmentation
+parameters that were not applied are still present, with a neutral value
+(``0``/``null``/``false``) rather than being omitted, so downstream
+consumers can rely on a stable schema across the whole dataset.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from src.analysis.difficulty_analyzer import DifficultyResult
+from src.analysis.geometry_analyzer import GeometryResult
+from src.augmentation.augmentation_engine import AugmentationRecord
+from src.config.schema import QualityConfig
+from src.placement.placement_engine import PlacementResult
+from src.rendering.renderer import RenderResult
+from src.signatures.signature_dataset import SignatureRecord
+from src.templates.template_provider import template_kind
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+
+def _json_default(obj: Any) -> Any:
+    if isinstance(obj, np.integer):
+        return int(obj)
+    if isinstance(obj, np.floating):
+        return float(obj)
+    if isinstance(obj, np.bool_):
+        return bool(obj)
+    if isinstance(obj, np.ndarray):
+        return obj.tolist()
+    if isinstance(obj, Path):
+        return obj.as_posix()
+    return str(obj)
+
+
+class MetadataExporter:
+    """Builds and writes the full per-sample metadata JSON document.
+
+    Example:
+        >>> exporter = MetadataExporter()
+        >>> metadata = exporter.build(...)
+        >>> exporter.export(metadata, "output/sample_000001.json")
+    """
+
+    def build(
+        self,
+        document_id: str,
+        template_id: str,
+        document_type: "str | None",
+        page: int,
+        page_size: tuple[int, int],
+        dpi: int,
+        signer_name: str,
+        signer_role: str,
+        signature_source: "SignatureRecord | None",
+        placement: PlacementResult,
+        render: RenderResult,
+        signature_aug: dict[str, AugmentationRecord],
+        document_aug: dict[str, AugmentationRecord],
+        geometry: GeometryResult,
+        difficulty: DifficultyResult,
+        quality_cfg: QualityConfig,
+        quality_values: dict[str, float],
+        seed: int,
+        sample_index: int,
+        verification: "dict[str, Any] | None" = None,
+    ) -> dict[str, Any]:
+        """Assemble the complete metadata dict for one generated sample."""
+        degraded = self._compute_degraded(quality_cfg)
+
+        augmentation_section = self._build_augmentation_section(placement, render, signature_aug, document_aug)
+
+        # Top-level convenience aliases, ported from projectA's leaner
+        # case-centric ground_truth.json schema (case_id/expected_verdict/
+        # expected_reject_code) so a reader doesn't have to know Venket's
+        # nested "verification.verdict" shape to answer "what should a
+        # verifier conclude about this document" -- the single most common
+        # question when reviewing generated test cases. Additive only: every
+        # field Venket's schema already had is still present unchanged
+        # below, this just surfaces a few of them a second time at the top
+        # level. `None` for generic (non-case-type) samples, same as
+        # `verification` itself being `None` in that situation.
+        expected_verdict = verification["verdict"] if verification else None
+        expected_reject_code = verification["reject_code"] if verification else None
+
+        metadata: dict[str, Any] = {
+            "case_id": document_id,
+            "expected_verdict": expected_verdict,
+            "expected_reject_code": expected_reject_code,
+            "document_id": document_id,
+            "template": template_id,
+            "template_kind": template_kind(template_id),
+            "document_type": document_type,
+            "page": page,
+            "page_size": list(page_size),
+            "dpi": dpi,
+            "signer": {
+                "name": signer_name,
+                "role": signer_role,
+            },
+            "signature_source": (
+                {
+                    "signature_id": signature_source.signature_id,
+                    "path": signature_source.path.as_posix(),
+                    "signer_group": signature_source.signer_group,
+                    "source_type": signature_source.source_type,
+                    "script": signature_source.script,
+                }
+                if signature_source is not None
+                else {"signature_id": None, "path": None, "signer_group": None, "source_type": "none", "script": None}
+            ),
+            "placement": {
+                "x_offset": placement.x_offset,
+                "y_offset": placement.y_offset,
+                "rotation_deg": round(placement.rotation_deg, 3),
+                "scale_factor": round(placement.scale_factor, 4),
+                "anchor_point": placement.anchor_point,
+                "margin_target_box": list(placement.target_box),
+                "misplaced": placement.misplaced,
+                "misplacement_dx": round(placement.misplacement_dx, 2),
+                "misplacement_dy": round(placement.misplacement_dy, 2),
+            },
+            "rendering": {
+                "blend_mode": render.blend_mode,
+                "ink_opacity": render.ink_opacity,
+                "blur_sigma": render.blur_sigma,
+                "feather_px": render.feather_px,
+                "scanner_noise": render.scanner_noise,
+            },
+            "difficulty": {
+                "score": difficulty.score,
+                "tier": difficulty.tier,
+                "misplacement": difficulty.misplacement,
+                "whitespace": difficulty.whitespace,
+                "faintness": difficulty.faintness,
+                "smallness": difficulty.smallness,
+                "low_texture": difficulty.low_texture,
+            },
+            "quality": {
+                "degraded": degraded,
+                "case_type": quality_cfg.case_type,
+                "notes": quality_cfg.notes,
+                "scanner_quality": quality_values["scanner_quality"],
+                "paper_quality": quality_values["paper_quality"],
+                "render_quality": quality_values["render_quality"],
+            },
+            "verification": verification or {
+                "case_type": None, "verdict": None, "reject_code": None,
+                "target_box_id": None, "expected_signer_group": None, "expected_signer_script": None,
+                "actual_signer_group": None, "actual_source_type": None, "actual_signer_script": None,
+                "companion": None,
+            },
+            "augmentation": augmentation_section,
+            "geometry": {
+                "box_px": list(geometry.target_box),
+                "signature_bbox_px": list(geometry.signature_bbox_px),
+                "target_box": list(geometry.target_box),
+                "actual_box": list(geometry.actual_box),
+                "search_box_px": list(geometry.search_box),
+                "signature_polygon_px": geometry.signature_polygon_px,
+                "convex_hull_px": geometry.convex_hull_px,
+                # Real per-ink-blob boundary contour(s) -- for visualization
+                # only (see GeometryResult.display_contours' docstring),
+                # never used in scoring; signature_polygon_px/convex_hull_px
+                # above remain the source of truth for every coverage metric.
+                "display_contours": geometry.display_contours,
+                "min_rotated_rect": geometry.min_rotated_rect,
+                "centroid_px": list(geometry.centroid_px),
+                "orientation_deg": geometry.orientation_deg,
+                "ink_pixels": geometry.ink_pixels,
+                "detected": geometry.found,
+            },
+            "metrics": {
+                "box_coverage_pct": geometry.box_coverage_pct,
+                "containment_pct": geometry.containment_pct,
+                "ink_fill_ratio": geometry.ink_fill_ratio,
+                "polygon_overlap_proxy": geometry.polygon_overlap_proxy,
+                "distance_from_box_center_px": geometry.distance_from_box_center_px,
+                "margin_utilization": geometry.margin_utilization,
+                "mean_dark_intensity": geometry.mean_dark_intensity,
+                "gray_stddev": geometry.gray_stddev,
+                "signature_area_ratio": geometry.signature_area_ratio,
+            },
+            "provenance": {
+                "master_seed": seed,
+                "sample_index": sample_index,
+            },
+        }
+        return metadata
+
+    @staticmethod
+    def _compute_degraded(quality_cfg: QualityConfig) -> bool:
+        case_type_flag = quality_cfg.case_type == "1d"
+        notes_flag = bool(quality_cfg.notes and "low-quality" in quality_cfg.notes.lower())
+        return bool(case_type_flag or notes_flag)
+
+    @staticmethod
+    def _build_augmentation_section(
+        placement: PlacementResult, render: RenderResult,
+        signature_aug: dict[str, AugmentationRecord], document_aug: dict[str, AugmentationRecord],
+    ) -> dict[str, Any]:
+        def rec(records: dict[str, AugmentationRecord], name: str) -> AugmentationRecord:
+            return records[name]
+
+        stroke_rec = rec(signature_aug, "stroke_dilation")
+        noise_rec = rec(document_aug, "scanner_noise")
+        stain_rec = rec(document_aug, "stain")
+        shadow_rec = rec(document_aug, "shadow")
+        coffee_rec = rec(document_aug, "coffee_stain")
+        crumple_rec = rec(document_aug, "crumple")
+        tilt_rec = rec(document_aug, "page_tilt")
+        discoloration_rec = rec(document_aug, "discoloration")
+        jpeg_rec = rec(document_aug, "jpeg_compression")
+
+        effects: dict[str, Any] = {}
+        for name, r in {**signature_aug, **document_aug}.items():
+            effects[name] = {
+                "enabled": r.enabled,
+                "applied": r.applied,
+                "strength": r.strength,
+                "probability": r.probability,
+                "seed": r.seed,
+                "params": r.params,
+            }
+
+        return {
+            "rotate_deg": round(placement.rotation_deg, 3),
+            "scale_factor": round(placement.scale_factor, 4),
+            "stroke": stroke_rec.strength if stroke_rec.applied else 0.0,
+            "noise_amount": noise_rec.strength if noise_rec.applied else 0.0,
+            "stain_type": stain_rec.params.get("resolved_type") if stain_rec.applied else None,
+            "stain_strength": stain_rec.strength if stain_rec.applied else 0.0,
+            "shadow_type": shadow_rec.params.get("resolved_type") if shadow_rec.applied else None,
+            "shadow_strength": shadow_rec.strength if shadow_rec.applied else 0.0,
+            "coffee_stain_strength": coffee_rec.strength if coffee_rec.applied else 0.0,
+            "crumple_strength": crumple_rec.strength if crumple_rec.applied else 0.0,
+            "tilt_deg": tilt_rec.params.get("tilt_deg", 0.0) if tilt_rec.applied else 0.0,
+            "discoloration_strength": discoloration_rec.strength if discoloration_rec.applied else 0.0,
+            "blur_sigma": render.blur_sigma,
+            "jpeg_quality": jpeg_rec.params.get("quality") if jpeg_rec.applied else None,
+            "scanner_noise": render.scanner_noise,
+            "effects": effects,
+        }
+
+    def export(self, metadata: dict[str, Any], path: "str | Path") -> None:
+        """Write the metadata dict to disk as pretty-printed JSON."""
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(metadata, indent=2, default=_json_default), encoding="utf-8")
diff --git a/unified_pipeline/src/placement/__init__.py b/unified_pipeline/src/placement/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..00452f9f7ce99c5345f13a5f3a0927076813dbc5
--- /dev/null
+++ b/unified_pipeline/src/placement/__init__.py
@@ -0,0 +1 @@
+"""Signature placement: fitting, transforming, and positioning a signature stamp inside a target box."""
diff --git a/unified_pipeline/src/placement/placement_engine.py b/unified_pipeline/src/placement/placement_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3cf017fe6972857575ec6465ef3e2e96904a8a
--- /dev/null
+++ b/unified_pipeline/src/placement/placement_engine.py
@@ -0,0 +1,226 @@
+"""Places a preprocessed signature stamp inside (or intentionally near) a target signature box.
+
+Handles aspect-ratio-preserving scale-to-fit, rotation, anchor-based
+positioning, sub-pixel offsets, and controlled "misplacement" (an
+intentional, probability-gated extra translation used to generate
+hard/misaligned samples for benchmarking).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import cv2
+import numpy as np
+
+from src.config.schema import PlacementConfig
+from src.utils.geometry_utils import Box
+from src.utils.logging_utils import get_logger
+from src.utils.random_utils import SeedManager, resolve_scalar_or_range
+from src.utils.transform_utils import rotate_and_scale_rgba
+
+logger = get_logger(__name__)
+
+AnchorOffsetFn = "dict[str, callable]"
+
+# The legibility floor itself now lives on PlacementConfig
+# (src/config/schema.py::PlacementConfig.min_signature_height_px, default
+# 28px -- see that field's docstring for the full deck-16px-vs-measured-28px
+# history) so it can be overridden the same config-driven way as every other
+# placement knob, instead of a module-level constant only this file could see.
+
+
+@dataclass
+class PlacementResult:
+    """Everything downstream stages need to know about how the signature was placed."""
+
+    layer: np.ndarray  # full-canvas-sized RGBA layer, transparent outside the signature
+    target_box: Box
+    intended_bbox: Box  # the signature's transformed bounding box in canvas coordinates
+    x_offset: float
+    y_offset: float
+    rotation_deg: float
+    scale_factor: float
+    anchor_point: str
+    misplaced: bool
+    misplacement_dx: float
+    misplacement_dy: float
+
+
+def _anchor_top_left(anchor: str, box_w: float, box_h: float, sig_w: float, sig_h: float) -> tuple[float, float]:
+    positions = {
+        "center": ((box_w - sig_w) / 2.0, (box_h - sig_h) / 2.0),
+        "top_left": (0.0, 0.0),
+        "top_right": (box_w - sig_w, 0.0),
+        "bottom_left": (0.0, box_h - sig_h),
+        "bottom_right": (box_w - sig_w, box_h - sig_h),
+        "top_center": ((box_w - sig_w) / 2.0, 0.0),
+        "bottom_center": ((box_w - sig_w) / 2.0, box_h - sig_h),
+        "left_center": (0.0, (box_h - sig_h) / 2.0),
+        "right_center": (box_w - sig_w, (box_h - sig_h) / 2.0),
+    }
+    return positions.get(anchor, positions["center"])
+
+
+class PlacementEngine:
+    """Fits, transforms, and positions a signature stamp relative to a target box.
+
+    Example:
+        >>> engine = PlacementEngine(cfg.placement)
+        >>> result = engine.place(signature_rgba, target_box=(100, 100, 300, 160),
+        ...                       canvas_size=(1024, 559), seed_manager=sm)
+    """
+
+    def __init__(self, config: PlacementConfig) -> None:
+        self.config = config
+
+    def place(
+        self,
+        signature_rgba: np.ndarray,
+        target_box: Box,
+        canvas_size: tuple[int, int],
+        seed_manager: SeedManager,
+    ) -> PlacementResult:
+        """Compute the placed signature layer for one sample.
+
+        Args:
+            signature_rgba: The tightly-cropped RGBA signature stamp from
+                :class:`~src.rendering.signature_preprocessor.SignaturePreprocessor`.
+            target_box: The ``(x1, y1, x2, y2)`` target signature field, in
+                canvas pixel coordinates.
+            canvas_size: ``(width, height)`` of the full document canvas.
+            seed_manager: Per-sample deterministic RNG provider.
+
+        Returns:
+            A :class:`PlacementResult` with the full-canvas RGBA layer ready
+            for blending, plus every resolved placement parameter for
+            metadata export.
+        """
+        cfg = self.config
+        rng = seed_manager.numpy_rng("placement")
+        canvas_w, canvas_h = canvas_size
+
+        rotation_deg = resolve_scalar_or_range(cfg.rotation_deg, rng)
+        scale_factor = resolve_scalar_or_range(cfg.scale_factor, rng)
+        x_offset = resolve_scalar_or_range(cfg.x_offset, rng)
+        y_offset = resolve_scalar_or_range(cfg.y_offset, rng)
+
+        x1, y1, x2, y2 = target_box
+        box_w, box_h = (x2 - x1), (y2 - y1)
+        margin_x, margin_y = box_w * cfg.margin, box_h * cfg.margin
+        inner_x1, inner_y1 = x1 + margin_x, y1 + margin_y
+        inner_w = max(box_w - 2 * margin_x, 1.0)
+        inner_h = max(box_h - 2 * margin_y, 1.0)
+
+        rotated = rotate_and_scale_rgba(signature_rgba, rotation_deg, 1.0)
+        sig_h, sig_w = rotated.shape[:2]
+
+        fit_scale = min(inner_w / max(sig_w, 1), inner_h / max(sig_h, 1))
+        final_scale = max(fit_scale * scale_factor, 1e-3)
+
+        # Never let the signature shrink below a legible minimum height, even
+        # inside a very short box (real "sign here" lines are often only
+        # 12-20px tall at document resolution) -- a genuinely tiny box should
+        # make the signature overflow it, not vanish into sub-pixel noise.
+        min_height_scale = cfg.min_signature_height_px / max(sig_h, 1)
+        final_scale = max(final_scale, min_height_scale)
+
+        placed = cv2.resize(
+            rotated, (max(int(round(sig_w * final_scale)), 1), max(int(round(sig_h * final_scale)), 1)),
+            interpolation=cv2.INTER_AREA if final_scale < 1.0 else cv2.INTER_CUBIC,
+        )
+        placed_h, placed_w = placed.shape[:2]
+
+        # A short "sign here" line (real bank forms are often only ~15-25px
+        # tall at this pipeline's working resolution) forces a large
+        # downscale of the signature stamp -- sometimes 15-20x when the
+        # source crop is a typical few-hundred-px-tall dataset image.
+        # INTER_AREA area-averages a multi-pixel-wide source stroke into a
+        # sub-pixel-wide destination stroke, which is the geometrically
+        # correct way to downscale, but it also dilutes that stroke's alpha
+        # well below fully opaque across most of its footprint -- a genuine
+        # ink stroke that was solidly opaque in the source crop can come
+        # out mostly under 30-40% opacity, rendering as a faint gray smudge
+        # rather than a smaller-but-still-dark signature (real signatures
+        # squeezed onto a short line stay dark, just compact). A plain
+        # linear rescale (matching a percentile of the resized alpha back
+        # up to the source's own level) was tried first and wasn't strong
+        # enough: even the SOURCE crop's own mid-tone pixels are already
+        # somewhat soft/anti-aliased from the original scan, so "matching
+        # the source" still left the bulk of the stroke under ~50% opacity.
+        # A gamma correction on the alpha channel instead boosts every
+        # partially-transparent pixel non-linearly (a 50%-opaque pixel
+        # becomes much more than 50% more opaque), which is what actually
+        # restores legibility. Gamma scales with how aggressive the
+        # downscale was (``final_scale`` itself, clamped) -- a sample that
+        # barely shrank gets ~no correction (gamma near 1, alpha^1 = alpha);
+        # one squeezed 10-20x onto a short line gets the strongest
+        # correction. Applied to alpha only, never RGB, so ink color is
+        # unaffected -- this makes thin/faint strokes fully opaque sooner,
+        # not darker in hue.
+        if final_scale < 1.0:
+            gamma = float(np.clip(final_scale * 3.0, 0.18, 1.0))
+            alpha_norm = placed[:, :, 3].astype(np.float32) / 255.0
+            placed = placed.copy()
+            placed[:, :, 3] = np.clip((alpha_norm**gamma) * 255.0, 0, 255).astype(np.uint8)
+
+        ax, ay = _anchor_top_left(cfg.anchor_point, inner_w, inner_h, placed_w, placed_h)
+        base_x = inner_x1 + ax + x_offset
+        base_y = inner_y1 + ay + y_offset
+
+        misplaced = bool(rng.uniform(0.0, 1.0) < cfg.misplacement_probability)
+        misplacement_dx = misplacement_dy = 0.0
+        if misplaced:
+            strength = resolve_scalar_or_range(cfg.misplacement_strength, rng)
+            max_shift = strength * max(box_w, box_h)
+            angle = rng.uniform(0, 2 * np.pi)
+            radius = rng.uniform(0.3, 1.0) * max_shift
+            misplacement_dx = float(np.cos(angle) * radius)
+            misplacement_dy = float(np.sin(angle) * radius)
+
+        final_x = base_x + misplacement_dx
+        final_y = base_y + misplacement_dy
+
+        if not cfg.allow_partial_outside and not misplaced:
+            final_x = float(np.clip(final_x, inner_x1, inner_x1 + max(inner_w - placed_w, 0)))
+            final_y = float(np.clip(final_y, inner_y1, inner_y1 + max(inner_h - placed_h, 0)))
+
+        # Hard safety clamp: never let the signature drift entirely off the physical page.
+        final_x = float(np.clip(final_x, -placed_w * 0.6, canvas_w - placed_w * 0.4))
+        final_y = float(np.clip(final_y, -placed_h * 0.6, canvas_h - placed_h * 0.4))
+
+        layer = np.zeros((canvas_h, canvas_w, 4), dtype=np.uint8)
+        self._paste(layer, placed, int(round(final_x)), int(round(final_y)))
+
+        intended_bbox: Box = (final_x, final_y, final_x + placed_w, final_y + placed_h)
+
+        return PlacementResult(
+            layer=layer,
+            target_box=target_box,
+            intended_bbox=intended_bbox,
+            x_offset=x_offset,
+            y_offset=y_offset,
+            rotation_deg=rotation_deg,
+            scale_factor=scale_factor,
+            anchor_point=cfg.anchor_point,
+            misplaced=misplaced,
+            misplacement_dx=misplacement_dx,
+            misplacement_dy=misplacement_dy,
+        )
+
+    @staticmethod
+    def _paste(canvas: np.ndarray, sprite: np.ndarray, x: int, y: int) -> None:
+        """Alpha-aware paste of ``sprite`` onto ``canvas`` at ``(x, y)``, clipped to bounds."""
+        canvas_h, canvas_w = canvas.shape[:2]
+        sprite_h, sprite_w = sprite.shape[:2]
+
+        dst_x1, dst_y1 = max(x, 0), max(y, 0)
+        dst_x2, dst_y2 = min(x + sprite_w, canvas_w), min(y + sprite_h, canvas_h)
+        if dst_x2 <= dst_x1 or dst_y2 <= dst_y1:
+            logger.warning("Placed signature falls entirely outside the canvas; skipping paste.")
+            return
+
+        src_x1, src_y1 = dst_x1 - x, dst_y1 - y
+        src_x2, src_y2 = src_x1 + (dst_x2 - dst_x1), src_y1 + (dst_y2 - dst_y1)
+
+        canvas[dst_y1:dst_y2, dst_x1:dst_x2] = sprite[src_y1:src_y2, src_x1:src_x2]
diff --git a/unified_pipeline/src/rendering/__init__.py b/unified_pipeline/src/rendering/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..77cd55b43955ae22b094a870b8c0f9bf3276848a
--- /dev/null
+++ b/unified_pipeline/src/rendering/__init__.py
@@ -0,0 +1 @@
+"""Signature preprocessing and document/signature compositing."""
diff --git a/unified_pipeline/src/rendering/printed_text.py b/unified_pipeline/src/rendering/printed_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..35242b6c3cf8a00aec034efa44b9bb9d55de67ea
--- /dev/null
+++ b/unified_pipeline/src/rendering/printed_text.py
@@ -0,0 +1,64 @@
+"""Renders a plain typed/printed name as an RGBA stamp (case_type "2b": no handwriting).
+
+Produces a tightly-cropped RGBA image shaped exactly like a
+:class:`~src.rendering.signature_preprocessor.SignaturePreprocessor` output,
+so it can flow through the existing
+:class:`~src.placement.placement_engine.PlacementEngine` and
+:class:`~src.rendering.renderer.Renderer` unchanged.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+_FONT_CANDIDATES: tuple[str, ...] = (
+    "C:/Windows/Fonts/arial.ttf",
+    "C:/Windows/Fonts/calibri.ttf",
+    "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+    "/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf",
+    "/System/Library/Fonts/Helvetica.ttc",
+)
+
+_FONT_SIZE_PX = 64
+_PAD_PX = 6
+
+
+def _load_font(size: int = _FONT_SIZE_PX) -> "ImageFont.ImageFont":
+    for path in _FONT_CANDIDATES:
+        try:
+            return ImageFont.truetype(path, size)
+        except OSError:
+            continue
+    logger.warning("No truetype font found; falling back to PIL's bitmap default font.")
+    return ImageFont.load_default()
+
+
+def render_printed_name_stamp(name: str, ink_color: tuple[int, int, int] = (15, 15, 20)) -> np.ndarray:
+    """Render ``name`` as plain typed text, returned as a tightly-cropped RGBA stamp.
+
+    Args:
+        name: The printed/typed name to render (no handwriting stroke texture).
+        ink_color: Flat RGB color for the text (matches the pipeline's ink color pool).
+
+    Returns:
+        An ``(H, W, 4)`` uint8 RGBA array: opaque text on a transparent background.
+    """
+    font = _load_font()
+
+    scratch = Image.new("RGBA", (8, 8), (0, 0, 0, 0))
+    scratch_draw = ImageDraw.Draw(scratch)
+    text_bbox = scratch_draw.textbbox((0, 0), name, font=font)
+    text_w = max(text_bbox[2] - text_bbox[0], 1)
+    text_h = max(text_bbox[3] - text_bbox[1], 1)
+
+    canvas_w, canvas_h = text_w + 2 * _PAD_PX, text_h + 2 * _PAD_PX
+    image = Image.new("RGBA", (canvas_w, canvas_h), (0, 0, 0, 0))
+    draw = ImageDraw.Draw(image)
+    draw.text((_PAD_PX - text_bbox[0], _PAD_PX - text_bbox[1]), name, font=font, fill=(*ink_color, 255))
+
+    return np.array(image, dtype=np.uint8)
diff --git a/unified_pipeline/src/rendering/renderer.py b/unified_pipeline/src/rendering/renderer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5905e1a133d25bfd599c04d213bc08f540117e8a
--- /dev/null
+++ b/unified_pipeline/src/rendering/renderer.py
@@ -0,0 +1,105 @@
+"""Composites the placed (and signature-augmented) RGBA signature layer onto the document.
+
+Implements the blend modes and realism passes required by the spec: multiply
+blend, darken blend, alpha blend, configurable ink opacity, feathering
+(alpha-edge softening), a slight blur to match scan resolution, sensor/scan
+noise, and anti-aliasing.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import cv2
+import numpy as np
+
+from src.config.schema import RenderingConfig
+from src.utils.logging_utils import get_logger
+from src.utils.random_utils import SeedManager, resolve_scalar_or_range
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class RenderResult:
+    """The blended document image plus the resolved rendering parameters."""
+
+    image: np.ndarray
+    blend_mode: str
+    ink_opacity: float
+    blur_sigma: float
+    feather_px: float
+    scanner_noise: float
+
+
+class Renderer:
+    """Blends a signature RGBA layer onto a document canvas with configurable realism.
+
+    Example:
+        >>> renderer = Renderer(cfg.rendering)
+        >>> result = renderer.blend(document_rgb, signature_layer_rgba, seed_manager)
+    """
+
+    def __init__(self, config: RenderingConfig) -> None:
+        self.config = config
+
+    def blend(
+        self, document_rgb: np.ndarray, signature_layer_rgba: np.ndarray, seed_manager: SeedManager,
+    ) -> RenderResult:
+        """Blend the signature layer onto the document and return the composited image.
+
+        Args:
+            document_rgb: The scanned document canvas, ``(H, W, 3)`` uint8.
+            signature_layer_rgba: A full-canvas-sized RGBA layer (as produced
+                by :class:`~src.placement.placement_engine.PlacementEngine`,
+                after any signature-level augmentation), transparent outside
+                the signature.
+            seed_manager: Per-sample deterministic RNG provider.
+
+        Returns:
+            A :class:`RenderResult` with the composited uint8 RGB image and
+            the resolved rendering parameters (for metadata export).
+        """
+        cfg = self.config
+        rng = seed_manager.numpy_rng("rendering")
+
+        ink_opacity = resolve_scalar_or_range(cfg.ink_opacity, rng)
+        blur_sigma = resolve_scalar_or_range(cfg.blur_sigma, rng)
+        feather_px = resolve_scalar_or_range(cfg.feather_px, rng)
+        scanner_noise = resolve_scalar_or_range(cfg.scanner_noise, rng)
+
+        layer = signature_layer_rgba.astype(np.float32)
+        if feather_px > 0:
+            layer[:, :, 3] = cv2.GaussianBlur(layer[:, :, 3], (0, 0), sigmaX=feather_px)
+        if blur_sigma > 0:
+            layer = cv2.GaussianBlur(layer, (0, 0), sigmaX=blur_sigma)
+
+        sig_rgb = layer[:, :, :3]
+        alpha = np.clip(layer[:, :, 3] / 255.0, 0.0, 1.0) * float(np.clip(ink_opacity, 0.0, 1.0))
+        alpha3 = alpha[:, :, None]
+
+        base = document_rgb.astype(np.float32)
+
+        if cfg.blend_mode == "multiply":
+            blended_pixel = base * (sig_rgb / 255.0)
+        elif cfg.blend_mode == "darken":
+            blended_pixel = np.minimum(base, sig_rgb)
+        else:  # alpha
+            blended_pixel = sig_rgb
+
+        composited = base * (1 - alpha3) + blended_pixel * alpha3
+
+        if scanner_noise > 0:
+            noise = rng.normal(0.0, scanner_noise * 255.0, size=composited.shape).astype(np.float32)
+            composited += noise
+
+        if cfg.anti_alias:
+            composited = cv2.GaussianBlur(composited, (0, 0), sigmaX=0.3)
+
+        out = np.clip(composited, 0, 255).astype(np.uint8)
+
+        return RenderResult(
+            image=out, blend_mode=cfg.blend_mode, ink_opacity=round(ink_opacity, 4),
+            blur_sigma=round(blur_sigma, 4), feather_px=round(feather_px, 4),
+            scanner_noise=round(scanner_noise, 4),
+        )
diff --git a/unified_pipeline/src/rendering/signature_preprocessor.py b/unified_pipeline/src/rendering/signature_preprocessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b7c03b19c25ab6aef249b93a73f3800b3f56336
--- /dev/null
+++ b/unified_pipeline/src/rendering/signature_preprocessor.py
@@ -0,0 +1,123 @@
+"""Signature cleaning: white-background removal and alpha-mask extraction.
+
+Public handwritten signature datasets are typically flat scans: dark ink
+strokes on an off-white/paper background, with no alpha channel. This module
+converts such a scan into a clean RGBA "stamp" -- background fully
+transparent, ink strokes opaque-to-translucent according to how dark the
+original pixel was (preserving natural stroke texture: thin/faint pen
+lift-offs stay translucent, heavy strokes stay opaque) -- ready to be
+transformed and composited onto a document by :class:`~src.placement.placement_engine.PlacementEngine`
+and :class:`~src.rendering.renderer.Renderer`.
+"""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+DEFAULT_INK_COLOR: tuple[int, int, int] = (18, 18, 24)
+
+
+class SignaturePreprocessor:
+    """Cleans a raw signature scan into a tightly-cropped, transparent RGBA stamp.
+
+    Example:
+        >>> pre = SignaturePreprocessor()
+        >>> rgba = pre.process(raw_signature_array)
+    """
+
+    def __init__(
+        self,
+        ink_color: tuple[int, int, int] = DEFAULT_INK_COLOR,
+        min_component_area: int = 4,
+        pad_px: int = 6,
+        alpha_gamma: float = 0.85,
+    ) -> None:
+        self.ink_color = ink_color
+        self.min_component_area = min_component_area
+        self.pad_px = pad_px
+        self.alpha_gamma = alpha_gamma
+
+    def process(self, raw_image: np.ndarray, ink_color: "tuple[int, int, int] | None" = None) -> np.ndarray:
+        """Convert a raw signature scan into a clean, tightly-cropped RGBA stamp.
+
+        Args:
+            raw_image: The raw signature image as loaded (grayscale ``(H, W)``,
+                RGB ``(H, W, 3)``, or RGBA ``(H, W, 4)`` uint8).
+            ink_color: Optional override for the flat ink color used for the
+                RGB channels (alpha still carries the natural stroke texture).
+
+        Returns:
+            An ``(H', W', 4)`` uint8 RGBA array, cropped tightly to the ink
+            content with a small transparent pad.
+        """
+        gray, existing_alpha = self._to_gray(raw_image)
+        gray = cv2.medianBlur(gray, 3)
+        alpha = self._compute_alpha(gray)
+        if existing_alpha is not None:
+            alpha = np.minimum(alpha, existing_alpha)
+        alpha = self._remove_speckles(alpha)
+        rgba = self._compose_rgba(alpha, ink_color or self.ink_color)
+        rgba = self._crop_to_content(rgba, pad=self.pad_px)
+        return rgba
+
+    @staticmethod
+    def _to_gray(raw_image: np.ndarray) -> tuple[np.ndarray, "np.ndarray | None"]:
+        if raw_image.ndim == 2:
+            return raw_image.copy(), None
+        if raw_image.shape[2] == 4:
+            rgb = raw_image[:, :, :3]
+            alpha = raw_image[:, :, 3]
+            gray = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY)
+            return gray, alpha
+        gray = cv2.cvtColor(raw_image[:, :, :3], cv2.COLOR_RGB2GRAY)
+        return gray, None
+
+    @staticmethod
+    def _compute_alpha(gray: np.ndarray) -> np.ndarray:
+        gray_f = gray.astype(np.float64)
+        background = float(np.percentile(gray_f, 92))
+        dark_pixels = gray_f[gray_f < background - 8]
+        dark_level = float(np.percentile(dark_pixels, 5)) if dark_pixels.size > 20 else max(background - 140, 0.0)
+        denom = max(background - dark_level, 1e-3)
+
+        raw = (background - gray_f) / denom
+        raw = np.clip(raw, 0.0, 1.0)
+        # Discard near-background noise (sub-threshold jitter from scanning artifacts).
+        raw[raw < 0.06] = 0.0
+        softened = raw**0.85
+        return (softened * 255.0).astype(np.uint8)
+
+    def _remove_speckles(self, alpha: np.ndarray) -> np.ndarray:
+        binary = (alpha > 15).astype(np.uint8)
+        n_labels, labels, stats, _ = cv2.connectedComponentsWithStats(binary, connectivity=8)
+        if n_labels <= 1:
+            return alpha
+        cleaned = alpha.copy()
+        for label in range(1, n_labels):
+            area = stats[label, cv2.CC_STAT_AREA]
+            if area < self.min_component_area:
+                cleaned[labels == label] = 0
+        return cleaned
+
+    @staticmethod
+    def _compose_rgba(alpha: np.ndarray, ink_color: tuple[int, int, int]) -> np.ndarray:
+        h, w = alpha.shape
+        rgb = np.empty((h, w, 3), dtype=np.uint8)
+        rgb[:, :] = ink_color
+        return np.dstack([rgb, alpha])
+
+    @staticmethod
+    def _crop_to_content(rgba: np.ndarray, pad: int) -> np.ndarray:
+        alpha = rgba[:, :, 3]
+        ys, xs = np.where(alpha > 4)
+        if ys.size == 0:
+            logger.warning("Signature preprocessing found no ink pixels; returning a 1x1 transparent stamp.")
+            return np.zeros((1, 1, 4), dtype=np.uint8)
+        y1, y2 = max(ys.min() - pad, 0), min(ys.max() + pad + 1, rgba.shape[0])
+        x1, x2 = max(xs.min() - pad, 0), min(xs.max() + pad + 1, rgba.shape[1])
+        return rgba[y1:y2, x1:x2].copy()
diff --git a/unified_pipeline/src/signatures/__init__.py b/unified_pipeline/src/signatures/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..da4feafc4233063f780bf480488958a55cca60a1
--- /dev/null
+++ b/unified_pipeline/src/signatures/__init__.py
@@ -0,0 +1 @@
+"""Signature dataset discovery and sampling."""
diff --git a/unified_pipeline/src/signatures/signature_dataset.py b/unified_pipeline/src/signatures/signature_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8a772c1e65754d6625d05bd8aa0b34509d670a3
--- /dev/null
+++ b/unified_pipeline/src/signatures/signature_dataset.py
@@ -0,0 +1,323 @@
+"""Recursive discovery of the public handwritten signature dataset.
+
+Scans ``dataset/`` recursively for signature images (PNG/JPG/JPEG/TIFF/BMP),
+building an internal database without requiring any particular naming
+convention or folder layout. Two conventions are detected automatically
+when present, but neither is required:
+
+* ``dataset/metadata.csv`` -- if present, associates files with a signer
+  identity via a ``filename``/``path`` column plus a ``signer``/``name``
+  column, and optionally a ``source_type`` and ``script`` column (the
+  handwriting script the signature is written in, e.g. ``hindi``,
+  ``bengali``, ``latin`` -- see ``dataset/hindi_bengali/`` for the bundled
+  multi-script signer pool).
+* Filenames matching CEDAR-style ``original_<signer>_<sample>.png`` /
+  ``forgeries_<signer>_<sample>.png`` (as found in the bundled dataset) --
+  used to recover a signer group id and genuine/forged label even without
+  a metadata file.
+
+Otherwise, each signature is assigned an automatically generated id and its
+immediate parent folder name is used as its signer group (matching the
+``dataset/personNNN/*.png`` layout described in the project spec).
+"""
+
+from __future__ import annotations
+
+import csv
+import re
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+from src.utils.image_io import discover_images
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+_CEDAR_PATTERN = re.compile(r"^(?P<prefix>[a-zA-Z]+)_(?P<signer>\d+)_(?P<sample>\d+)$")
+
+# SigNet layout: <signet_dir>/{train,test}/<id>/*.png (genuine),
+# <signet_dir>/{train,test}/<id>_forg/*.png (forged of writer <id>).
+_SIGNET_FORGED_DIR_PATTERN = re.compile(r"^(?P<writer>\d+)_forg$")
+
+# BHSig260 layout: <bhsig260_X_dir>/BHSig260-X/<writer_id>/{H|B}-S-<writer_id>-{G|F}-<sample>.tif
+_BHSIG260_PATTERN = re.compile(r"^[HB]-S-(?P<writer>\d+)-(?P<label>[GF])-(?P<sample>\d+)$")
+
+# GPDS layout: <gpds_dir>/.../test/<writer_id>/{genuine,forge}/*.jpg
+_GPDS_LABEL_DIRS = {"genuine": "genuine", "forge": "forged"}
+
+
+@dataclass(frozen=True)
+class SignatureRecord:
+    """One discovered signature image and its inferred identity metadata."""
+
+    signature_id: str
+    path: Path
+    signer_group: str
+    source_type: str  # "genuine" | "forged" | "unknown"
+    script: str = "latin"  # handwriting script, e.g. "latin" | "hindi" | "bengali"
+
+
+class SignatureDataset:
+    """Discovers and indexes the signature image dataset for deterministic sampling.
+
+    Example:
+        >>> ds = SignatureDataset("dataset")
+        >>> ds.discover()
+        >>> record = ds.sample(rng, source_filter="genuine")
+    """
+
+    def __init__(
+        self, dataset_dir: "str | Path",
+        signet_dir: "str | Path | None" = None,
+        gpds_dir: "str | Path | None" = None,
+        bhsig260_hindi_dir: "str | Path | None" = None,
+        bhsig260_bengali_dir: "str | Path | None" = None,
+    ) -> None:
+        self.dataset_dir = Path(dataset_dir)
+        # Additive signature sources beyond `dataset_dir` (CEDAR genuine/
+        # forged + the curated hindi_bengali/metadata.csv sample). Each is
+        # optional and skipped gracefully if not given or not present on
+        # disk -- same policy projectA's dataset_registry.py used for
+        # BHSig260 -- so this repo works whether or not a caller has all
+        # four raw datasets checked out locally. See discover() below and
+        # `unified_pipeline/README.md` for the directory layout each expects.
+        self.signet_dir = Path(signet_dir) if signet_dir else None
+        self.gpds_dir = Path(gpds_dir) if gpds_dir else None
+        self.bhsig260_hindi_dir = Path(bhsig260_hindi_dir) if bhsig260_hindi_dir else None
+        self.bhsig260_bengali_dir = Path(bhsig260_bengali_dir) if bhsig260_bengali_dir else None
+        self._records: "list[SignatureRecord] | None" = None
+        self._by_id: "dict[str, SignatureRecord] | None" = None
+
+    def discover(self) -> list[SignatureRecord]:
+        """Recursively discover all signature images and build the internal database."""
+        if self._records is not None:
+            return self._records
+
+        paths = discover_images(self.dataset_dir)
+        if not paths:
+            raise FileNotFoundError(f"No signature images found under '{self.dataset_dir}'.")
+
+        metadata_map = self._load_metadata_csv()
+
+        records: list[SignatureRecord] = []
+        for idx, path in enumerate(paths):
+            rel = path.relative_to(self.dataset_dir)
+            stem = path.stem
+
+            meta = metadata_map.get(rel.as_posix()) or metadata_map.get(path.name)
+            signer_group = None
+            source_type = "unknown"
+
+            match = _CEDAR_PATTERN.match(stem)
+            if match:
+                signer_group = f"signer_{int(match.group('signer')):04d}"
+                prefix = match.group("prefix").lower()
+                if "orig" in prefix or "genuine" in prefix:
+                    source_type = "genuine"
+                elif "forg" in prefix:
+                    source_type = "forged"
+
+            if meta and meta.get("signer"):
+                signer_group = meta["signer"]
+            if meta and meta.get("source_type"):
+                source_type = meta["source_type"]
+
+            if signer_group is None:
+                signer_group = rel.parent.as_posix() if rel.parent != Path(".") else "unassigned"
+
+            if source_type == "unknown":
+                lowered = rel.as_posix().lower()
+                if "genuine" in lowered or "original" in lowered:
+                    source_type = "genuine"
+                elif "forg" in lowered:
+                    source_type = "forged"
+
+            signature_id = meta.get("signature_id") if meta else None
+            if not signature_id:
+                signature_id = f"sig_{idx:06d}"
+
+            script = (meta.get("script") if meta else None) or "latin"
+
+            records.append(SignatureRecord(signature_id, path, signer_group, source_type, script))
+
+        records.extend(self._discover_signet())
+        records.extend(self._discover_bhsig260(self.bhsig260_hindi_dir, "hindi"))
+        records.extend(self._discover_bhsig260(self.bhsig260_bengali_dir, "bengali"))
+        records.extend(self._discover_gpds())
+
+        self._records = records
+        self._by_id = {r.signature_id: r for r in records}
+        n_genuine = sum(1 for r in records if r.source_type == "genuine")
+        n_forged = sum(1 for r in records if r.source_type == "forged")
+        n_groups = len({r.signer_group for r in records})
+        logger.info(
+            "Discovered %d signature images (%d genuine, %d forged, %d unknown) across %d signer group(s).",
+            len(records), n_genuine, n_forged, len(records) - n_genuine - n_forged, n_groups,
+        )
+        return records
+
+    def _discover_signet(self) -> "list[SignatureRecord]":
+        """SigNet: ``{train,test}/<id>/*`` (genuine) + ``{train,test}/<id>_forg/*`` (forged).
+
+        Ported from ``projectA/utils/dataset_registry.py``'s SigNet wiring
+        (that repo's PRIMARY signature source). ``signer_group`` is prefixed
+        ``signet_<id>`` so it can never collide with a CEDAR-derived
+        ``signer_<NNNN>`` group even though both use small zero-padded
+        integers -- see ``src/verification/registry.py``'s
+        ``_AUTO_GROUP_PATTERN`` for the display-name implication of that.
+        """
+        if self.signet_dir is None or not self.signet_dir.exists():
+            return []
+        records: "list[SignatureRecord]" = []
+        idx = 0
+        for split_dir in sorted(p for p in self.signet_dir.iterdir() if p.is_dir()):
+            for writer_dir in sorted(p for p in split_dir.iterdir() if p.is_dir()):
+                forg_match = _SIGNET_FORGED_DIR_PATTERN.match(writer_dir.name)
+                if forg_match:
+                    writer_id, source_type = forg_match.group("writer"), "forged"
+                elif writer_dir.name.isdigit():
+                    writer_id, source_type = writer_dir.name, "genuine"
+                else:
+                    continue
+                signer_group = f"signet_{int(writer_id):04d}"
+                for path in discover_images(writer_dir):
+                    records.append(SignatureRecord(f"signet_{idx:06d}", path, signer_group, source_type, "latin"))
+                    idx += 1
+        if records:
+            logger.info("Discovered %d SigNet signature images under %s", len(records), self.signet_dir)
+        return records
+
+    def _discover_bhsig260(self, root: "Path | None", script: str) -> "list[SignatureRecord]":
+        """BHSig260: ``<root>/BHSig260-<Script>/<writer_id>/{H|B}-S-<writer_id>-{G|F}-<sample>.tif``.
+
+        ``root`` is the raw download directory (e.g. ``BHSig260-Hindi/``,
+        which itself contains a nested ``BHSig260-Hindi/`` subfolder in the
+        bundled layout) -- searched recursively so either the outer or the
+        writer-id-folder level being the literal ``root`` both work.
+        """
+        if root is None or not root.exists():
+            return []
+        records: "list[SignatureRecord]" = []
+        idx = 0
+        for path in discover_images(root):
+            match = _BHSIG260_PATTERN.match(path.stem)
+            if not match:
+                continue
+            writer_id = match.group("writer")
+            source_type = "genuine" if match.group("label") == "G" else "forged"
+            signer_group = f"bhsig260_{script}_{int(writer_id):04d}"
+            records.append(SignatureRecord(f"bhsig260_{script}_{idx:06d}", path, signer_group, source_type, script))
+            idx += 1
+        if records:
+            logger.info("Discovered %d BHSig260-%s signature images under %s", len(records), script.capitalize(), root)
+        return records
+
+    def _discover_gpds(self) -> "list[SignatureRecord]":
+        """GPDS: ``.../<writer_id>/{genuine,forge}/*``.
+
+        Note: unlike SigNet/BHSig260/CEDAR, GPDS is not validated/wired into
+        either sibling pipeline's live generation path today (flagged
+        unused in both ``projectA/utils/prepare_signatures.py`` and this
+        merge's own exploration) -- it is included here per "use all the
+        dataset", but spot-check a generated batch that draws from it
+        before trusting it the way the other three sources are trusted.
+        """
+        if self.gpds_dir is None or not self.gpds_dir.exists():
+            return []
+        records: "list[SignatureRecord]" = []
+        idx = 0
+        for writer_dir in sorted(p for p in self.gpds_dir.rglob("*") if p.is_dir() and p.name.isdigit()):
+            for label_dir_name, source_type in _GPDS_LABEL_DIRS.items():
+                label_dir = writer_dir / label_dir_name
+                if not label_dir.is_dir():
+                    continue
+                signer_group = f"gpds_{int(writer_dir.name):04d}"
+                for path in discover_images(label_dir):
+                    records.append(SignatureRecord(f"gpds_{idx:06d}", path, signer_group, source_type, "latin"))
+                    idx += 1
+        if records:
+            logger.info("Discovered %d GPDS signature images under %s", len(records), self.gpds_dir)
+        return records
+
+    def _load_metadata_csv(self) -> dict:
+        csv_path = self.dataset_dir / "metadata.csv"
+        if not csv_path.exists():
+            return {}
+        mapping: dict = {}
+        with csv_path.open("r", encoding="utf-8", newline="") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                key = row.get("path") or row.get("filename") or row.get("file")
+                if not key:
+                    continue
+                normalized = {k.strip().lower(): (v.strip() if v else v) for k, v in row.items()}
+                signer = normalized.get("signer") or normalized.get("name") or normalized.get("signer_name")
+                mapping[key] = {
+                    "signer": signer,
+                    "source_type": normalized.get("source_type") or normalized.get("type"),
+                    "signature_id": normalized.get("signature_id") or normalized.get("id"),
+                    "script": normalized.get("script"),
+                }
+        if mapping:
+            logger.info("Loaded signature metadata for %d entries from %s", len(mapping), csv_path)
+        return mapping
+
+    def get(self, signature_id: str) -> SignatureRecord:
+        """Return a signature record by id."""
+        self.discover()
+        if signature_id not in self._by_id:
+            raise KeyError(f"Unknown signature id '{signature_id}'.")
+        return self._by_id[signature_id]
+
+    def filtered_records(self, source_filter: str = "any") -> list[SignatureRecord]:
+        """Return records matching ``source_filter`` ("genuine" | "forged" | "any")."""
+        records = self.discover()
+        if source_filter == "any":
+            return records
+        filtered = [r for r in records if r.source_type == source_filter]
+        if not filtered:
+            logger.warning(
+                "No signatures match source_filter='%s'; falling back to the full dataset.",
+                source_filter,
+            )
+            return records
+        return filtered
+
+    def sample(self, rng: np.random.Generator, source_filter: str = "genuine") -> SignatureRecord:
+        """Deterministically sample one signature record using the given seeded generator."""
+        pool = self.filtered_records(source_filter)
+        idx = int(rng.integers(0, len(pool)))
+        return pool[idx]
+
+    def signer_groups(self) -> list[str]:
+        """Return every distinct signer group id, sorted for deterministic indexing."""
+        return sorted({r.signer_group for r in self.discover()})
+
+    def records_for_group(self, signer_group: str, source_filter: str = "any") -> list[SignatureRecord]:
+        """Return records belonging to one signer group, optionally filtered by source type."""
+        records = [r for r in self.discover() if r.signer_group == signer_group]
+        if source_filter == "any":
+            return records
+        filtered = [r for r in records if r.source_type == source_filter]
+        if not filtered:
+            logger.warning(
+                "Signer group '%s' has no '%s' records; falling back to all its records.",
+                signer_group, source_filter,
+            )
+            return records
+        return filtered
+
+    def sample_from_group(
+        self, rng: np.random.Generator, signer_group: str, source_filter: str = "genuine",
+    ) -> SignatureRecord:
+        """Deterministically sample one signature record from a specific signer group."""
+        pool = self.records_for_group(signer_group, source_filter)
+        idx = int(rng.integers(0, len(pool)))
+        return pool[idx]
+
+    def script_for_group(self, signer_group: str) -> str:
+        """Return the handwriting script associated with a signer group (all its records share one)."""
+        records = self.records_for_group(signer_group, source_filter="any")
+        return records[0].script if records else "latin"
diff --git a/unified_pipeline/src/templates/__init__.py b/unified_pipeline/src/templates/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fecabb1dc447255b0e848dc42b991b64afb1d62f
--- /dev/null
+++ b/unified_pipeline/src/templates/__init__.py
@@ -0,0 +1 @@
+"""Template discovery, annotation loading, and CV-based signature box detection."""
diff --git a/unified_pipeline/src/templates/annotation_schema.py b/unified_pipeline/src/templates/annotation_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ba520221dddd92b750d650478cdf9b133e7946b
--- /dev/null
+++ b/unified_pipeline/src/templates/annotation_schema.py
@@ -0,0 +1,101 @@
+"""Pydantic schema for template annotation files (``annotations/<template_id>.json``)."""
+
+from __future__ import annotations
+
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+
+class SignatureBoxAnnotation(BaseModel):
+    """A single signature field on a page, in pixel coordinates of the template image."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    id: str
+    x1: float
+    y1: float
+    x2: float
+    y2: float
+    role: str = "Signer"
+    name: str = "Signer"
+
+    @model_validator(mode="after")
+    def _normalize_box(self) -> "SignatureBoxAnnotation":
+        x1, x2 = sorted((self.x1, self.x2))
+        y1, y2 = sorted((self.y1, self.y2))
+        self.x1, self.x2, self.y1, self.y2 = x1, x2, y1, y2
+        if x2 <= x1 or y2 <= y1:
+            raise ValueError(f"Signature box '{self.id}' has non-positive area: {self.as_tuple()}")
+        return self
+
+    def as_tuple(self) -> tuple[float, float, float, float]:
+        """Return the box as ``(x1, y1, x2, y2)``."""
+        return (self.x1, self.y1, self.x2, self.y2)
+
+    @property
+    def width(self) -> float:
+        return self.x2 - self.x1
+
+    @property
+    def height(self) -> float:
+        return self.y2 - self.y1
+
+
+class CosmeticFieldAnnotation(BaseModel):
+    """A cosmetic (non-scored) text field's pre-measured anchor line, in
+    pixel coordinates of the template image.
+
+    Measured ONCE via ``scripts/detect_cosmetic_field_coordinates.py``
+    (see that script's docstring for the exact method: a hand-picked
+    generous search region, narrowed by the same classical-CV horizontal-
+    line detector used for signature-box auto-detection) and cached here,
+    so filling a field at generation time is a plain coordinate lookup --
+    no per-sample image analysis -- the same way ``signature_boxes`` above
+    already work.
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    field: str  # e.g. "name", "name_sig2", "company_name" -- matches src/cosmetics/cosmetic_fields.py's dispatch
+    mode: Literal["above_line", "below_line"]
+    line_x: float
+    line_y: float
+    line_w: float
+    box_id: "str | None" = None
+    """Which ``signature_boxes[].id`` this field's text belongs to (e.g. a
+    "Print Name:" line directly tied to one specific signatory), so the
+    right signer's name gets drawn on the right line on templates with more
+    than one signature block. ``None`` for entity-level fields with no
+    single owner (e.g. ``company_name``), which just use the sample's
+    primary/default name-generation instead."""
+
+
+class TemplatePageAnnotation(BaseModel):
+    """A single page of a template document."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    page: int = 1
+    page_size: tuple[int, int]
+    dpi: int = 300
+    signature_boxes: list[SignatureBoxAnnotation] = Field(default_factory=list)
+    cosmetic_fields: list[CosmeticFieldAnnotation] = Field(default_factory=list)
+
+
+class TemplateAnnotation(BaseModel):
+    """Full annotation for one template document (possibly multi-page)."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    id: str
+    source_image: "str | None" = None
+    document_type: "str | None" = None
+    pages: list[TemplatePageAnnotation]
+    auto_detected: bool = False
+
+    def page_by_number(self, page: int) -> TemplatePageAnnotation:
+        for p in self.pages:
+            if p.page == page:
+                return p
+        raise KeyError(f"Template '{self.id}' has no page {page}")
diff --git a/unified_pipeline/src/templates/box_detector.py b/unified_pipeline/src/templates/box_detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..014c2063a1bc71f14993fc15d0df9fc03f2f64a6
--- /dev/null
+++ b/unified_pipeline/src/templates/box_detector.py
@@ -0,0 +1,133 @@
+"""Mode 1: automatic signature-field detection via classical computer vision.
+
+Bank document signature fields are almost always denoted by a horizontal
+"sign here" rule (an underscore/line) with a caption below it ("Signature",
+"Authorized Signatory", ...). This detector isolates long, thin horizontal
+line segments via morphological opening, filters them by geometry, and
+places a signature box directly above each surviving line -- the same
+region a human would use to place a pen.
+
+This is a heuristic, not a learned layout model; it is intentionally used
+only as a *fallback* when no manual annotation file exists (Mode 2 is
+preferred whenever available, see :class:`~src.templates.template_loader.TemplateLoader`).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import cv2
+import numpy as np
+
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class DetectedLine:
+    x: int
+    y: int
+    w: int
+    h: int
+
+    @property
+    def aspect_ratio(self) -> float:
+        return self.w / max(self.h, 1)
+
+
+def _binarize(gray: np.ndarray) -> np.ndarray:
+    """Return a binary ink mask (255 = ink) using adaptive thresholding."""
+    blurred = cv2.GaussianBlur(gray, (3, 3), 0)
+    binary = cv2.adaptiveThreshold(
+        blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 25, 10,
+    )
+    return binary
+
+
+def _detect_horizontal_lines(binary: np.ndarray, min_width_frac: float = 0.08) -> list[DetectedLine]:
+    """Detect long horizontal rule segments (signature/blank-field underscores)."""
+    h, w = binary.shape[:2]
+    kernel_w = max(int(w * min_width_frac), 15)
+    horiz_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_w, 1))
+    opened = cv2.morphologyEx(binary, cv2.MORPH_OPEN, horiz_kernel, iterations=1)
+    opened = cv2.dilate(opened, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 1)), iterations=1)
+
+    contours, _ = cv2.findContours(opened, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    lines: list[DetectedLine] = []
+    for c in contours:
+        x, y, cw, ch = cv2.boundingRect(c)
+        line = DetectedLine(x, y, cw, ch)
+        if line.w < w * min_width_frac:
+            continue
+        if line.aspect_ratio < 6.0:
+            continue
+        if line.h > 12:
+            continue
+        lines.append(line)
+    return lines
+
+
+def _merge_nearby_lines(lines: list[DetectedLine], y_tol: int = 8) -> list[DetectedLine]:
+    """Merge line fragments that are on the same visual row into one segment."""
+    if not lines:
+        return []
+    lines = sorted(lines, key=lambda l: (l.y, l.x))
+    merged: list[DetectedLine] = [lines[0]]
+    for line in lines[1:]:
+        last = merged[-1]
+        if abs(line.y - last.y) <= y_tol and line.x <= last.x + last.w + 40:
+            new_x2 = max(last.x + last.w, line.x + line.w)
+            merged[-1] = DetectedLine(last.x, min(last.y, line.y), new_x2 - last.x, max(last.h, line.h))
+        else:
+            merged.append(line)
+    return merged
+
+
+def detect_signature_boxes(
+    image_rgb: np.ndarray,
+    max_boxes: int = 4,
+    box_height_frac: float = 0.55,
+) -> list[tuple[float, float, float, float]]:
+    """Detect likely signature box regions above horizontal rule lines.
+
+    Args:
+        image_rgb: The template page as an ``(H, W, 3)`` uint8 array.
+        max_boxes: Maximum number of candidate boxes to return (keeps the
+            widest/most line-like candidates, which correlate with genuine
+            signature/date fields rather than table gridlines).
+        box_height_frac: Height of the returned box above the line, as a
+            fraction of the line's width (wider lines get proportionally
+            taller signature boxes, capped below).
+
+    Returns:
+        A list of ``(x1, y1, x2, y2)`` boxes in image pixel coordinates,
+        sorted left-to-right, top-to-bottom. Empty if nothing was detected.
+    """
+    h, w = image_rgb.shape[:2]
+    gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
+    binary = _binarize(gray)
+    lines = _detect_horizontal_lines(binary)
+    lines = _merge_nearby_lines(lines)
+
+    if not lines:
+        logger.warning("Automatic signature box detection found no candidate lines.")
+        return []
+
+    lines = sorted(lines, key=lambda l: l.w, reverse=True)[: max_boxes * 2]
+
+    boxes: list[tuple[float, float, float, float]] = []
+    for line in lines:
+        box_h = min(max(int(line.w * box_height_frac * 0.25), 20), 80)
+        x1 = float(max(line.x, 0))
+        x2 = float(min(line.x + line.w, w))
+        y2 = float(line.y)
+        y1 = float(max(line.y - box_h, 0))
+        if (x2 - x1) < 20 or (y2 - y1) < 8:
+            continue
+        boxes.append((x1, y1, x2, y2))
+
+    boxes = boxes[:max_boxes]
+    boxes.sort(key=lambda b: (round(b[1] / 50.0), b[0]))
+    logger.info("Automatic detection found %d candidate signature box(es).", len(boxes))
+    return boxes
diff --git a/unified_pipeline/src/templates/procedural_template_provider.py b/unified_pipeline/src/templates/procedural_template_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..09ddc57d891e2cd58f3eed9f67c25dcbbe27c395
--- /dev/null
+++ b/unified_pipeline/src/templates/procedural_template_provider.py
@@ -0,0 +1,471 @@
+"""Second :class:`~src.templates.template_loader.TemplateLoader`-shaped
+template source: matplotlib-drawn bank documents instead of scanned images.
+
+Ported from ``projectA/utils/templatemaker.py`` (the sibling pipeline this
+repo was merged with -- see ``unified_pipeline/README.md``). Two deliberate
+simplifications versus the original:
+
+1. **Degradation is dropped entirely.** The original ``templatemaker.py``
+   drew a clean document then immediately applied its own stain/shadow/
+   tilt/blur pipeline inline. That pipeline is formula-identical to this
+   repo's own :class:`~src.augmentation.augmentation_engine.AugmentationEngine`
+   document effects (confirmed in ``projectA/handoff/SCHEMA_HANDOFF.md``),
+   so degradation now happens exactly once, downstream, the same way for
+   both scanned and procedural templates -- this module only ever produces
+   the *clean* base page.
+2. **Each of the 15 template configs is rendered once and cached to PNG**
+   (``<cache_dir>/<template_id>.png``), then treated
+   exactly like a scanned template from that point on: a fixed image with
+   fixed signature-box coordinates, reused across every sample that draws
+   this template id. This is what lets it satisfy the same
+   discover/get/list_ids/random_template contract as
+   :class:`~src.templates.template_loader.TemplateLoader` (see
+   :mod:`src.templates.template_provider`) with no changes to
+   :mod:`src.generator`'s per-sample pipeline.
+"""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+import matplotlib.patches as patches
+import matplotlib.pyplot as plt
+import numpy as np
+
+from src.templates.annotation_schema import SignatureBoxAnnotation, TemplateAnnotation, TemplatePageAnnotation
+from src.templates.template_loader import Template
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+# Single source of truth for data-inches -> pixel conversion, same values as
+# the figure setup in _render_clean_document() below (kept in this file only
+# -- there is no shared geometry module between this and the scanned-template
+# path, since the two providers' documents are unrelated canvases).
+_DPI = 200
+_PAGE_W_IN, _PAGE_H_IN = 8.5, 11.0
+
+
+def _to_px(x_in: float, y_in: float) -> "tuple[int, int]":
+    return (int(round(x_in * _DPI)), int(round((_PAGE_H_IN - y_in) * _DPI)))
+
+
+def _box_px(x0_in: float, y0_in: float, x1_in: float, y1_in: float) -> "tuple[int, int, int, int]":
+    """``(x0,y0,x1,y1)`` in data-inches, ``y0 < y1`` -- returns pixel ``(x1px,y1px,x2px,y2px)``."""
+    px_top_left = _to_px(x0_in, y1_in)
+    px_bot_right = _to_px(x1_in, y0_in)
+    return (px_top_left[0], px_top_left[1], px_bot_right[0], px_bot_right[1])
+
+
+def _slug(text: str) -> str:
+    text = text.strip().lower().replace("/", " ")
+    text = re.sub(r"\s+", "_", text)
+    return re.sub(r"[^a-z0-9_]", "", text)
+
+
+def get_signature_boxes(doc_type: str, num_sigs: int, sig_roles: "list[str] | None") -> "list[dict]":
+    """Pure geometry function -- computes signature-box role/label/pixel-box
+    from the same constants :func:`_render_clean_document` draws with, so
+    the drawn label and the returned metadata cannot drift apart.
+
+    Box aspect ratio (~2.5:1) is tuned to the SigNet/BHSig260 signature
+    dataset's own ink aspect ratio, per ``SCHEMA_HANDOFF.md`` section 2b --
+    NOT portable to a different signature dataset without re-measuring.
+    """
+    entries = []
+
+    if doc_type.lower() == "cheque voucher":
+        y_offset = 6.2
+        entries.append({
+            "role_label": "AUTHORIZED SIGNATORY / DRAWER MANDATE",
+            "box_in": (5.3, y_offset + 0.55, 6.3, y_offset + 0.95),
+        })
+    elif num_sigs == 1:
+        role = sig_roles[0] if sig_roles else "PRIMARY AUTHORIZED SIGNATORY"
+        entries.append({
+            "role_label": f"SIGNATURE OF {role.upper()} *",
+            "box_in": (0.5, 1.2, 1.5, 1.6),
+        })
+    else:
+        role1 = sig_roles[0] if (sig_roles and len(sig_roles) > 0) else "AUTHORIZED SIGNATORY 1"
+        role2 = sig_roles[1] if (sig_roles and len(sig_roles) > 1) else "AUTHORIZED SIGNATORY 2"
+        entries.append({
+            "role_label": f"1. SIGNATURE: {role1.upper()} *",
+            "box_in": (0.5, 1.2, 1.5, 1.6),
+        })
+        entries.append({
+            "role_label": f"2. SIGNATURE: {role2.upper()} *",
+            "box_in": (4.8, 1.2, 5.8, 1.6),
+        })
+
+    boxes = []
+    for entry in entries:
+        x0, y0, x1, y1 = entry["box_in"]
+        boxes.append({
+            "role": _slug(entry["role_label"]),
+            "role_label": entry["role_label"],
+            "box": _box_px(x0, y0, x1, y1),
+        })
+    return boxes
+
+
+def get_field_boxes(doc_type: str) -> "list[dict]":
+    """Pure geometry function -- mirrors :func:`get_signature_boxes` but for
+    cosmetic (non-scored) field cells. Returns ``[{"field": name, "box_in":
+    (x0,y0,x1,y1)}, ...]`` in data-inches. Consumed by
+    :mod:`src.cosmetics.cosmetic_fields`.
+    """
+    if doc_type.lower() == "cheque voucher":
+        y_offset = 6.2
+        return [
+            {"field": "payee_name", "box_in": (1.4, y_offset + 1.8, 5.9, y_offset + 2.05)},
+            {"field": "date", "box_in": (6.1, y_offset + 2.58, 6.1 + 0.23 * 8, y_offset + 2.8)},
+            {"field": "amount_words", "box_in": (0.5, y_offset + 1.2, 5.8, y_offset + 1.45)},
+            {"field": "amount_numeric", "box_in": (6.1, y_offset + 1.05, 8.0, y_offset + 1.5)},
+        ]
+
+    boxes = [
+        {"field": "full_legal_name", "box_in": (0.5, 8.4, 4.25, 9.1)},
+        {"field": "registration_no", "box_in": (4.25, 8.4, 8.0, 9.1)},
+        {"field": "country_of_incorporation", "box_in": (0.5, 7.7, 4.25, 8.4)},
+        {"field": "tax_jurisdiction", "box_in": (4.25, 7.7, 8.0, 8.4)},
+        {"field": "registered_address", "box_in": (0.5, 6.42, 8.0, 7.0)},
+    ]
+    for i in range(3):
+        y_row = 5.4 - 0.45 * i
+        boxes.append({"field": f"swift_row{i}", "box_in": (3.4, y_row - 0.05, 5.2, y_row + 0.15)})
+        boxes.append({"field": f"currency_row{i}", "box_in": (5.4, y_row - 0.05, 6.4, y_row + 0.15)})
+        boxes.append({"field": f"amount_row{i}", "box_in": (6.6, y_row - 0.05, 8.0, y_row + 0.15)})
+        boxes.append({"field": f"checkbox_row{i}", "box_in": (0.6, y_row, 0.72, y_row + 0.12)})
+    return boxes
+
+
+# --------------------------------------------------------------------------- #
+# Drawing helpers -- each draws one recurring visual element and returns its
+# own data-inch box (unused by the render itself, kept for parity with the
+# source module in case a future caller wants finer-grained boxes).
+# --------------------------------------------------------------------------- #
+
+def _draw_labeled_line(ax, x0, x1, y, label_text, label_dy=0.15, label_kwargs=None, line_kwargs=None):
+    label_kwargs = {"fontsize": 6.5, "fontname": "DejaVu Sans", "weight": "bold", "color": "#1e293b", **(label_kwargs or {})}
+    line_kwargs = {"color": "#334155", "linewidth": 0.7, **(line_kwargs or {})}
+    ax.plot([x0, x1], [y, y], **line_kwargs)
+    ax.text(x0, y + label_dy, label_text, **label_kwargs)
+
+
+def _draw_checkbox(ax, x, y, size=0.12, edgecolor="#475569", linewidth=0.6):
+    ax.add_patch(patches.Rectangle((x, y), size, size, fill=False, edgecolor=edgecolor, linewidth=linewidth))
+
+
+def _draw_bordered_box(ax, x, y, w, h, facecolor="none", edgecolor="#0f172a", linewidth=1.2):
+    ax.add_patch(patches.Rectangle((x, y), w, h, fill=(facecolor != "none"), facecolor=facecolor, edgecolor=edgecolor, linewidth=linewidth))
+
+
+def _draw_date_grid(ax, x_start, y, n_boxes=8, box_w=0.2, box_h=0.22, gap=0.23, edgecolor="#475569", linewidth=0.6):
+    x = x_start
+    for _ in range(n_boxes):
+        ax.add_patch(patches.Rectangle((x, y), box_w, box_h, fill=False, edgecolor=edgecolor, linewidth=linewidth))
+        x += gap
+
+
+def _draw_text_field(ax, x, y, text, **text_kwargs):
+    ax.text(x, y, text, **text_kwargs)
+
+
+def _draw_section_header(ax, x, y, w, h, text, bg_color="#1e293b", text_color="#ffffff", fontsize=7.5, text_dx=0.1, text_dy=0.06):
+    ax.add_patch(patches.Rectangle((x, y), w, h, facecolor=bg_color, edgecolor="none"))
+    ax.text(x + text_dx, y + text_dy, text, fontsize=fontsize, weight="bold", fontname="DejaVu Sans", color=text_color)
+
+
+def _draw_account_row(ax, y_row, label_text, edgecolor="#cbd5e1"):
+    ax.plot([0.5, 8.0], [y_row - 0.1, y_row - 0.1], color="#e2e8f0", linewidth=0.5)
+    _draw_checkbox(ax, 0.6, y_row, size=0.12, edgecolor="#64748b")
+    _draw_text_field(ax, 0.8, y_row + 0.02, label_text, fontsize=6.5, fontname="DejaVu Sans", color="#334155")
+    _draw_bordered_box(ax, 3.4, y_row - 0.05, 1.8, 0.2, edgecolor=edgecolor, linewidth=0.5)
+    _draw_bordered_box(ax, 5.4, y_row - 0.05, 1.0, 0.2, edgecolor=edgecolor, linewidth=0.5)
+    _draw_bordered_box(ax, 6.6, y_row - 0.05, 1.4, 0.2, edgecolor=edgecolor, linewidth=0.5)
+
+
+def _draw_signature_slot(ax, x0, x1, y, role_label, subtext):
+    ax.plot([x0, x1], [y, y], color="#1e293b", linewidth=0.8)
+    ax.text(x0, y - 0.15, role_label, fontsize=6.5, fontname="DejaVu Sans", weight="bold", color="#1e293b")
+    ax.text(x0, y - 0.3, subtext, fontsize=5.5, fontname="DejaVu Sans", color="#94a3b8")
+
+
+def _draw_paragraph(ax, x, y_start, text, max_chars_per_line=135, line_height=0.15, text_kwargs=None):
+    text_kwargs = {"fontsize": 5.8, "fontname": "DejaVu Sans", "color": "#475569", "va": "top", **(text_kwargs or {})}
+    words = text.split()
+    lines, current_line = [], ""
+    for word in words:
+        if len(current_line + " " + word) < max_chars_per_line:
+            current_line += " " + word
+        else:
+            lines.append(current_line)
+            current_line = word
+    lines.append(current_line)
+    y_text = y_start
+    for line in lines:
+        ax.text(x, y_text, line.strip(), **text_kwargs)
+        y_text -= line_height
+
+
+def _render_clean_document(
+    bank_name: str, doc_type: str, title: str,
+    num_sigs: int = 1, sig_roles: "list[str] | None" = None, currency_symbol: str = "S$",
+) -> "np.ndarray":
+    """Draws one clean (undegraded) A4 document and returns it as an RGB array.
+
+    No stain/shadow/tilt/blur is applied here -- see the module docstring.
+    """
+    signature_boxes = get_signature_boxes(doc_type, num_sigs, sig_roles)
+
+    fig, ax = plt.subplots(figsize=(_PAGE_W_IN, _PAGE_H_IN), dpi=_DPI)
+    ax.set_xlim(0, _PAGE_W_IN)
+    ax.set_ylim(0, _PAGE_H_IN)
+    ax.axis("off")
+    ax.set_position([0, 0, 1, 1])
+    fig.patch.set_facecolor("#fdfcf7")
+    ax.set_facecolor("#fdfcf7")
+
+    if doc_type.lower() == "cheque voucher":
+        y_offset = 6.2
+
+        _draw_bordered_box(ax, 0.25, y_offset + 0.0, 8.0, 3.3, edgecolor="#334155", linewidth=0.9)
+        _draw_bordered_box(ax, 0.28, y_offset + 0.03, 7.94, 3.24, edgecolor="#64748b", linewidth=0.4)
+
+        for y_line in np.linspace(y_offset + 0.4, y_offset + 3.0, 6):
+            ax.plot([0.4, 8.15], [y_line, y_line], color="#94a3b8", alpha=0.04, linewidth=0.5)
+
+        _draw_text_field(ax, 0.5, y_offset + 2.8, bank_name.upper(), fontsize=11, weight="bold", fontname="DejaVu Sans", color="#0f172a")
+        _draw_text_field(ax, 0.5, y_offset + 2.55, "COMMERCIAL CLEARING BRANCH // INSTRUMENT CODE", fontsize=6, fontname="DejaVu Sans", color="#475569", weight="bold")
+
+        _draw_text_field(ax, 5.6, y_offset + 2.65, "DATE:", fontsize=7, weight="bold", fontname="DejaVu Sans", color="#334155")
+        _draw_date_grid(ax, 6.1, y_offset + 2.58, n_boxes=8, box_w=0.2, box_h=0.22, gap=0.23)
+
+        _draw_text_field(ax, 0.5, y_offset + 1.85, "PAY TO THE\nORDER OF", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#1e293b")
+        ax.plot([1.4, 5.9], [y_offset + 1.8, y_offset + 1.8], color="#334155", linewidth=0.7)
+        _draw_text_field(ax, 6.0, y_offset + 1.82, "OR BEARER", fontsize=6.5, fontname="DejaVu Sans", weight="medium", color="#475569")
+        _draw_checkbox(ax, 6.8, y_offset + 1.8, size=0.12)
+
+        _draw_text_field(ax, 0.5, y_offset + 1.25, "AMOUNT IN WORDS", fontsize=6.0, fontname="DejaVu Sans", color="#64748b", style="italic")
+        ax.plot([0.5, 5.8], [y_offset + 1.2, y_offset + 1.2], color="#334155", linewidth=0.7)
+
+        _draw_text_field(ax, 6.1, y_offset + 1.55, "DOLLARS ONLY", fontsize=6.5, fontname="DejaVu Sans", weight="bold", color="#1e293b", ha="left")
+
+        _draw_bordered_box(ax, 6.1, y_offset + 1.05, 1.9, 0.45, facecolor="#fafafa", edgecolor="#0f172a", linewidth=1.2)
+        _draw_text_field(ax, 6.2, y_offset + 1.18, currency_symbol, fontsize=11, weight="black", fontname="DejaVu Sans", color="#0f172a")
+        ax.plot([6.6, 7.85], [y_offset + 1.12, y_offset + 1.12], color="#94a3b8", linestyle=":", linewidth=0.5)
+
+        _draw_labeled_line(
+            ax, 5.3, 7.95, y_offset + 0.55, signature_boxes[0]["role_label"],
+            label_dy=-0.15, label_kwargs={"fontsize": 6, "fontname": "DejaVu Sans", "weight": "bold", "color": "#334155"},
+            line_kwargs={"color": "#0f172a", "linewidth": 0.8},
+        )
+
+        # Cosmetic-only, fixed for the life of this cached template image
+        # (see the module docstring) -- NOT re-derived from the pipeline's
+        # seed, same as how a real scanned template's printed serial number
+        # never changes across samples either.
+        cheque_num = f"{np.random.randint(100000, 999999)}"
+        bank_code = f"{np.random.randint(100, 999)}"
+        branch_code = f"{np.random.randint(100, 999)}"
+        account_num = f"{np.random.randint(1000000, 9999999)}"
+        micr_string = f"| {cheque_num} | {bank_code} : {branch_code} : {account_num} | -02"
+        spaced_micr_string = "  ".join(list(micr_string))
+        _draw_text_field(ax, 4.25, y_offset + 0.18, spaced_micr_string, fontsize=6.0, fontname="DejaVu Sans Mono", ha="center", weight="bold", color="#0f172a", wrap=False)
+
+    else:
+        _draw_text_field(ax, 0.5, 10.4, bank_name.upper(), fontsize=13, weight="bold", fontname="DejaVu Sans", color="#0f172a")
+        _draw_text_field(ax, 0.5, 10.2, f"{doc_type.upper()} // TRANSACTIONS & RECORD MANDATE", fontsize=7, fontname="DejaVu Sans", color="#475569", weight="bold")
+        _draw_text_field(ax, 8.0, 10.4, f"FORM ID: {get_bank_info(bank_name)['code']}-{np.random.randint(10000, 99999)}", fontsize=7.5, fontname="DejaVu Sans", ha="right", weight="bold", color="#1e293b")
+        _draw_text_field(ax, 8.0, 10.25, "CENTRAL ARCHIVE SCANNER LAYER 6.2", fontsize=6, fontname="DejaVu Sans", ha="right", color="#64748b")
+        ax.plot([0.5, 8.0], [10.0, 10.0], color="#0f172a", linewidth=1.2)
+
+        _draw_text_field(ax, 0.5, 9.65, title.upper(), fontsize=10, weight="bold", fontname="DejaVu Sans", color="#0f172a")
+        _draw_text_field(ax, 0.5, 9.48, "Please complete in BLOCK CAPITALS. Fields marked with an asterisk (*) are mandatory under compliance protocols.", fontsize=6.5, fontname="DejaVu Sans", color="#64748b", style="italic")
+
+        _draw_section_header(ax, 0.5, 9.1, 7.5, 0.22, "SECTION 1: APPLICANT & LEGAL ENTITY PROFILE")
+        ax.plot([0.5, 8.0], [8.4, 8.4], color="#cbd5e1", linewidth=0.5)
+        ax.plot([0.5, 8.0], [7.7, 7.7], color="#cbd5e1", linewidth=0.5)
+        ax.plot([0.5, 8.0], [7.0, 7.0], color="#cbd5e1", linewidth=0.5)
+        ax.plot([4.25, 4.25], [7.0, 9.1], color="#cbd5e1", linewidth=0.5)
+
+        _draw_text_field(ax, 0.6, 8.8, "1.1 FULL LEGAL NAME OF ENTITY / INDIVIDUAL *", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#475569")
+        _draw_text_field(ax, 4.35, 8.8, "1.2 REGISTRATION NO. / UEN / NRIC *", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#475569")
+        _draw_text_field(ax, 0.6, 8.1, "1.3 COUNTRY OF INCORPORATION / NATIONALITY", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#475569")
+        _draw_text_field(ax, 4.35, 8.1, "1.4 TAX RESIDENCY JURISDICTION (CRS/FATCA) *", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#475569")
+        _draw_text_field(ax, 0.6, 6.7, "1.5 REGISTERED BUSINESS ADDRESS / RESIDENTIAL PLACE OF PERMANENCE", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#475569")
+
+        _draw_section_header(ax, 0.5, 6.2, 7.5, 0.22, "SECTION 2: SETTLEMENT PARAMETERS & TRANSACTION ROUTING", bg_color="#334155")
+        _draw_text_field(ax, 0.6, 5.95, "ACCOUNT SPECIFICATION", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#1e293b")
+        _draw_text_field(ax, 3.5, 5.95, "SWIFT / ROUTING CODE", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#1e293b")
+        _draw_text_field(ax, 5.5, 5.95, "CURRENCY", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#1e293b")
+        _draw_text_field(ax, 6.8, 5.95, "AMOUNT / LIMIT", fontsize=6.5, weight="bold", fontname="DejaVu Sans", color="#1e293b")
+        ax.plot([0.5, 8.0], [5.85, 5.85], color="#475569", linewidth=0.8)
+
+        y_row = 5.4
+        for i in range(3):
+            _draw_account_row(ax, y_row, f"Primary Operative Account Portfolio #{i + 1}")
+            y_row -= 0.45
+
+        _draw_text_field(ax, 0.5, 4.0, "SECTION 3: COMPLIANCE MANDATE & LEGAL DECLARATION", fontsize=7.5, weight="bold", fontname="DejaVu Sans", color="#0f172a")
+        ax.plot([0.5, 8.0], [3.9, 3.9], color="#0f172a", linewidth=0.8)
+
+        fine_print = (
+            "The undersigned corporate officers/account holders hereby declare and guarantee that the funds utilized within this execution cycle "
+            "do not originate from any illicit source and comply fully with the Anti-Money Laundering (AML) Regulations, Monetary Authorities acts, "
+            "and international legal sanctions frameworks. We acknowledge that the bank reserves the absolute operational right to suspend, freeze, "
+            "or close the account infrastructure without prior notice if systemic non-compliance or fraudulent variances are flagged by audit overlays."
+        )
+        _draw_paragraph(ax, 0.5, 3.7, fine_print, max_chars_per_line=135, line_height=0.15)
+
+        ax.plot([0.5, 8.0], [2.2, 2.2], color="#cbd5e1", linewidth=0.6)
+        if num_sigs == 1:
+            _draw_signature_slot(ax, 0.5, 4.0, 1.2, signature_boxes[0]["role_label"], "Verify signature specimen records match template")
+            _draw_signature_slot(ax, 4.8, 8.0, 1.2, "COMPANY RUBBER STAMP & DATE", "Affix institutional seal alongside timestamp entry")
+        else:
+            _draw_signature_slot(ax, 0.5, 4.0, 1.2, signature_boxes[0]["role_label"], "First corporate executive authentication index")
+            _draw_signature_slot(ax, 4.8, 8.0, 1.2, signature_boxes[1]["role_label"], "Joint account controller verification index")
+
+        _draw_text_field(ax, 0.5, 0.4, f"Form Reference Code: CR-OPM_SG_v9.4 // Confidential Audit Document // Printed {np.random.randint(10, 28)}-06-2026", fontsize=5.5, fontname="DejaVu Sans", color="#94a3b8")
+        _draw_text_field(ax, 8.0, 0.4, "Page 1 of 1", fontsize=5.5, fontname="DejaVu Sans", color="#94a3b8", ha="right")
+
+    fig.canvas.draw()
+    img = np.asarray(fig.canvas.buffer_rgba()).copy()[:, :, :3]
+    plt.close(fig)
+    return img
+
+
+# --------------------------------------------------------------------------- #
+# Bank reference data -- single source of truth for anything that must stay
+# consistent with a template's "bank" field (real SG SWIFT/BIC codes).
+# --------------------------------------------------------------------------- #
+
+BANK_INFO = {
+    "DBS Bank Singapore": {"swift": "DBSSSGSGXXX", "code": "DBS"},
+    "OCBC Bank Singapore": {"swift": "OCBCSGSGXXX", "code": "OCBC"},
+    "United Overseas Bank (UOB)": {"swift": "UOVBSGSGXXX", "code": "UOB"},
+    "Standard Chartered SG": {"swift": "SCBLSGSGXXX", "code": "SCB"},
+    "HSBC Singapore Branch": {"swift": "HSBCSGSGXXX", "code": "HSBC"},
+}
+
+
+def get_bank_info(bank_name: str) -> "dict[str, str]":
+    """Looks up BANK_INFO, falling back to a clearly-synthetic placeholder
+    (never a real other bank's code) for an unregistered bank name."""
+    return BANK_INFO.get(bank_name, {"swift": "UNKNSGSGXXX", "code": bank_name[:4].upper()})
+
+
+# 15 template configs: 5 document types x 3 banks, ported from
+# projectA/utils/templatemaker.py::dataset_directory. "stain"/"tilt"/"shadow"
+# are kept only as descriptive metadata (informational, e.g. for a UI that
+# wants to say "this template historically leaned toward X") -- they no
+# longer drive rendering, since degradation is AugmentationEngine's job now.
+TEMPLATE_CONFIGS: "list[dict]" = [
+    {"bank": "DBS Bank Singapore", "type": "Cheque Voucher", "title": "Corporate Inward Remittance Settlement Advice", "sigs": 1, "roles": ["Account Drawer"], "stain": False, "tilt": 0.0, "shadow": None, "entity_type": "corporate"},
+    {"bank": "DBS Bank Singapore", "type": "Cheque Voucher", "title": "Priority Wealth Clearing & Issuance Request", "sigs": 1, "roles": ["Vault Release Manager"], "stain": True, "tilt": -0.4, "shadow": "edge", "entity_type": "individual"},
+    {"bank": "DBS Bank Singapore", "type": "Cheque Voucher", "title": "Commercial Cross-Border Clearing Mandate", "sigs": 2, "roles": ["Managing Signatory", "Joint Controller"], "stain": False, "tilt": 0.8, "shadow": "corner", "entity_type": "corporate"},
+    {"bank": "OCBC Bank Singapore", "type": "Loan Agreement", "title": "Commercial Credit Line Facility Indenture Contract", "sigs": 2, "roles": ["Primary Debtor", "Corporate Guarantor"], "stain": True, "tilt": -0.9, "shadow": None, "entity_type": "corporate"},
+    {"bank": "OCBC Bank Singapore", "type": "Loan Agreement", "title": "E-Business Working Capital Loan Disbursal Form", "sigs": 1, "roles": ["Managing Director"], "stain": False, "tilt": 0.3, "shadow": "edge", "entity_type": "corporate"},
+    {"bank": "OCBC Bank Singapore", "type": "Loan Agreement", "title": "Syndicated Infrastructure Credit Drawdown Request", "sigs": 2, "roles": ["Authorized Representative", "Lead Arranger Officer"], "stain": False, "tilt": -0.6, "shadow": "corner", "entity_type": "corporate"},
+    {"bank": "United Overseas Bank (UOB)", "type": "Account Opening", "title": "Corporate Accounts & Cash Management Enrolment Mandate", "sigs": 2, "roles": ["Chief Executive Officer", "Company Secretary"], "stain": True, "tilt": 1.1, "shadow": None, "entity_type": "corporate"},
+    {"bank": "United Overseas Bank (UOB)", "type": "Account Opening", "title": "Global Wealth Asset Management Account Profile", "sigs": 1, "roles": ["Primary Beneficiary"], "stain": False, "tilt": -0.3, "shadow": "edge", "entity_type": "individual"},
+    {"bank": "United Overseas Bank (UOB)", "type": "Account Opening", "title": "Non-Resident Foreign Currency Account Mandate", "sigs": 2, "roles": ["Applicant Agent", "Internal Compliance Controller"], "stain": False, "tilt": -1.1, "shadow": "corner", "entity_type": "individual"},
+    {"bank": "Standard Chartered SG", "type": "Promissory Note", "title": "Unconditional Term Promissory Payment Obligation", "sigs": 1, "roles": ["Promisor / Issuing Principal"], "stain": True, "tilt": 0.0, "shadow": "edge", "entity_type": "individual"},
+    {"bank": "Standard Chartered SG", "type": "Promissory Note", "title": "Secured Corporate Liquidity Settlement Instrument", "sigs": 2, "roles": ["Issuing Debtor", "Board Witness Exec"], "stain": False, "tilt": 1.2, "shadow": None, "entity_type": "corporate"},
+    {"bank": "Standard Chartered SG", "type": "Promissory Note", "title": "Structured Asset Backed Credit Promissory Note", "sigs": 1, "roles": ["Authorized Chief Treasurer"], "stain": False, "tilt": -0.4, "shadow": "corner", "entity_type": "corporate"},
+    {"bank": "HSBC Singapore Branch", "type": "Bank Statement", "title": "Commercial Portfolio Cash Flow & Swift Transaction Audit", "sigs": 1, "roles": ["Reviewing Desk Auditor"], "stain": False, "tilt": 0.0, "shadow": "edge", "entity_type": "corporate"},
+    {"bank": "HSBC Singapore Branch", "type": "Bank Statement", "title": "Treasury Sweeping & Global Liquidity Settlement Ledger", "sigs": 2, "roles": ["Operational Maker", "Authorized Checker"], "stain": True, "tilt": -0.7, "shadow": "corner", "entity_type": "corporate"},
+    {"bank": "HSBC Singapore Branch", "type": "Bank Statement", "title": "Institutional Multi-Asset Valuation & Custody Statement", "sigs": 1, "roles": ["Beneficiary Sign-off"], "stain": False, "tilt": 0.5, "shadow": None, "entity_type": "individual"},
+]
+
+
+class ProceduralTemplateProvider:
+    """Matplotlib-drawn documents, satisfying the same discover/get/list_ids/
+    random_template contract as :class:`~src.templates.template_loader.TemplateLoader`.
+
+    Example:
+        >>> provider = ProceduralTemplateProvider(cache_dir="templates/procedural")
+        >>> templates = provider.discover()
+        >>> tmpl = provider.get("proc_doc_01")
+    """
+
+    def __init__(self, cache_dir: "str | Path") -> None:
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+        self._templates: "dict[str, Template] | None" = None
+        self._configs: "dict[str, dict] | None" = None
+
+    def discover(self) -> "dict[str, Template]":
+        if self._templates is not None:
+            return self._templates
+
+        templates: "dict[str, Template]" = {}
+        configs: "dict[str, dict]" = {}
+        for idx, cfg in enumerate(TEMPLATE_CONFIGS, start=1):
+            template_id = f"proc_doc_{idx:02d}"
+            templates[template_id] = self._build_template(template_id, cfg)
+            configs[template_id] = cfg
+            logger.info(
+                "Registered procedural template '%s' -> %s (%s / %s, %d signature box(es))",
+                template_id, templates[template_id].image_path, cfg["bank"], cfg["type"], cfg["sigs"],
+            )
+
+        self._templates = templates
+        self._configs = configs
+        return templates
+
+    def get(self, template_id: str) -> Template:
+        templates = self.discover()
+        if template_id not in templates:
+            raise KeyError(f"Unknown template id '{template_id}'. Known: {sorted(templates)}")
+        return templates[template_id]
+
+    def list_ids(self) -> "list[str]":
+        return sorted(self.discover().keys())
+
+    def random_template(self, rng: "np.random.Generator") -> Template:
+        ids = self.list_ids()
+        choice = ids[int(rng.integers(0, len(ids)))]
+        return self.get(choice)
+
+    def field_boxes_for(self, template_id: str) -> "dict | None":
+        """Not part of the common TemplateProvider contract -- returns the raw
+        ``dataset_directory``-style config (bank/type/entity_type/...) for a
+        template, which :mod:`src.cosmetics.cosmetic_fields` needs.
+        :class:`~src.templates.template_loader.TemplateLoader` has no
+        equivalent; callers must use ``getattr(provider, "field_boxes_for",
+        None)`` rather than assuming every provider has this method.
+        """
+        self.discover()
+        return self._configs.get(template_id)
+
+    def _build_template(self, template_id: str, cfg: dict) -> Template:
+        image_path = self.cache_dir / f"{template_id}.png"
+        if not image_path.exists():
+            img = _render_clean_document(
+                bank_name=cfg["bank"], doc_type=cfg["type"], title=cfg["title"],
+                num_sigs=cfg["sigs"], sig_roles=cfg["roles"],
+            )
+            plt.imsave(str(image_path), img)
+
+        boxes_meta = get_signature_boxes(cfg["type"], cfg["sigs"], cfg["roles"])
+        signature_boxes = [
+            SignatureBoxAnnotation(
+                id=f"sig{i + 1}",
+                x1=b["box"][0], y1=b["box"][1], x2=b["box"][2], y2=b["box"][3],
+                role=(cfg["roles"][i] if i < len(cfg["roles"]) else b["role_label"]),
+                name=(cfg["roles"][i] if i < len(cfg["roles"]) else b["role_label"]),
+            )
+            for i, b in enumerate(boxes_meta)
+        ]
+        page = TemplatePageAnnotation(
+            page=1, page_size=(int(_PAGE_W_IN * _DPI), int(_PAGE_H_IN * _DPI)), dpi=_DPI,
+            signature_boxes=signature_boxes,
+        )
+        annotation = TemplateAnnotation(
+            id=template_id, source_image=str(image_path.as_posix()), document_type=cfg["type"],
+            pages=[page], auto_detected=False,
+        )
+        return Template(template_id=template_id, image_path=image_path, annotation=annotation)
diff --git a/unified_pipeline/src/templates/template_loader.py b/unified_pipeline/src/templates/template_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb243bab6f9f1f567d8ef983695059f3297defa8
--- /dev/null
+++ b/unified_pipeline/src/templates/template_loader.py
@@ -0,0 +1,159 @@
+"""Template discovery and annotation resolution.
+
+Scans ``image/`` recursively for scanned bank document templates, assigns
+each a stable ``template_id`` (``bank_doc_01``, ``bank_doc_02``, ...) based
+on a deterministic sort of their paths, and resolves signature-box
+annotations for each using, in order of preference:
+
+1. **Mode 2 (preferred when present):** a manually authored
+   ``annotations/<template_id>.json`` file.
+2. **Mode 1 (fallback):** automatic CV-based detection
+   (:mod:`src.templates.box_detector`), whose result is cached to
+   ``annotations/<template_id>.json`` so subsequent runs reuse it without
+   re-running detection.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+
+from src.templates.annotation_schema import TemplateAnnotation, TemplatePageAnnotation, SignatureBoxAnnotation
+from src.templates.box_detector import detect_signature_boxes
+from src.utils.image_io import discover_images, load_image_rgb
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class Template:
+    """A discovered template document: its image path and resolved annotation."""
+
+    template_id: str
+    image_path: Path
+    annotation: TemplateAnnotation
+
+
+class TemplateLoader:
+    """Discovers bank document templates and resolves their signature-box annotations.
+
+    Example:
+        >>> loader = TemplateLoader(image_dir="image", annotations_dir="annotations")
+        >>> templates = loader.discover()
+        >>> tmpl = loader.get("bank_doc_01")
+    """
+
+    def __init__(
+        self, image_dir: "str | Path", annotations_dir: "str | Path",
+        excluded_ids: "frozenset[str] | set[str]" = frozenset(),
+    ) -> None:
+        self.image_dir = Path(image_dir)
+        self.annotations_dir = Path(annotations_dir)
+        self.annotations_dir.mkdir(parents=True, exist_ok=True)
+        # Ids to leave out of the discovered pool entirely (see
+        # paths.excluded_template_ids in src/config/schema.py). Ids are
+        # assigned by sorted-path enumeration BEFORE filtering, so
+        # excluding one id never shifts any other template's id -- e.g.
+        # excluding "bank_doc_08" still leaves "bank_doc_09" as
+        # "bank_doc_09", not renumbered down to "bank_doc_08".
+        self.excluded_ids = frozenset(excluded_ids)
+        self._templates: "dict[str, Template] | None" = None
+
+    def discover(self) -> "dict[str, Template]":
+        """Discover all templates under ``image_dir`` and resolve their annotations.
+
+        Returns:
+            An ordered mapping of ``template_id -> Template``, sorted by id.
+        """
+        if self._templates is not None:
+            return self._templates
+
+        image_paths = discover_images(self.image_dir)
+        if not image_paths:
+            raise FileNotFoundError(
+                f"No supported template images found under '{self.image_dir}'. "
+                "Expected PNG/JPG/JPEG/TIFF/BMP files."
+            )
+
+        templates: "dict[str, Template]" = {}
+        for idx, image_path in enumerate(image_paths, start=1):
+            template_id = f"bank_doc_{idx:02d}"
+            if template_id in self.excluded_ids:
+                logger.info("Skipped template '%s' -> %s (excluded via paths.excluded_template_ids)", template_id, image_path)
+                continue
+            annotation = self._resolve_annotation(template_id, image_path)
+            templates[template_id] = Template(template_id, image_path, annotation)
+            logger.info(
+                "Registered template '%s' -> %s (%d signature box(es), auto_detected=%s)",
+                template_id, image_path, sum(len(p.signature_boxes) for p in annotation.pages),
+                annotation.auto_detected,
+            )
+
+        self._templates = templates
+        return templates
+
+    def get(self, template_id: str) -> Template:
+        """Return a discovered template by id, raising ``KeyError`` if unknown."""
+        templates = self.discover()
+        if template_id not in templates:
+            raise KeyError(f"Unknown template id '{template_id}'. Known: {sorted(templates)}")
+        return templates[template_id]
+
+    def list_ids(self) -> list[str]:
+        """Return all discovered template ids, sorted."""
+        return sorted(self.discover().keys())
+
+    def random_template(self, rng: np.random.Generator) -> Template:
+        """Pick a uniformly random template using the given seeded generator."""
+        ids = self.list_ids()
+        choice = ids[int(rng.integers(0, len(ids)))]
+        return self.get(choice)
+
+    def _resolve_annotation(self, template_id: str, image_path: Path) -> TemplateAnnotation:
+        annotation_path = self.annotations_dir / f"{template_id}.json"
+        if annotation_path.exists():
+            data = json.loads(annotation_path.read_text(encoding="utf-8"))
+            return TemplateAnnotation.model_validate(data)
+
+        logger.warning(
+            "No manual annotation found for '%s'; falling back to automatic CV detection.",
+            template_id,
+        )
+        annotation = self._auto_detect_annotation(template_id, image_path)
+        annotation_path.write_text(
+            json.dumps(annotation.model_dump(), indent=2), encoding="utf-8"
+        )
+        logger.info("Cached auto-detected annotation to %s", annotation_path)
+        return annotation
+
+    def _auto_detect_annotation(self, template_id: str, image_path: Path) -> TemplateAnnotation:
+        image = load_image_rgb(image_path)
+        h, w = image.shape[:2]
+        boxes = detect_signature_boxes(image)
+
+        if not boxes:
+            # Last-resort fallback: a generic box in the lower-right quadrant,
+            # where signature fields conventionally sit on Western bank forms.
+            boxes = [(w * 0.55, h * 0.80, w * 0.92, h * 0.90)]
+            logger.warning(
+                "'%s': no lines detected; using generic lower-right fallback box.", template_id
+            )
+
+        signature_boxes = [
+            SignatureBoxAnnotation(
+                id=f"sig{i + 1}", x1=x1, y1=y1, x2=x2, y2=y2, role="Signer", name="Signer",
+            )
+            for i, (x1, y1, x2, y2) in enumerate(boxes)
+        ]
+        page = TemplatePageAnnotation(page=1, page_size=(w, h), dpi=300, signature_boxes=signature_boxes)
+        return TemplateAnnotation(
+            id=template_id,
+            source_image=str(image_path.as_posix()),
+            document_type="unknown",
+            pages=[page],
+            auto_detected=True,
+        )
diff --git a/unified_pipeline/src/templates/template_provider.py b/unified_pipeline/src/templates/template_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..44d8a0352a7caef7b979849f58e8efb3966595b1
--- /dev/null
+++ b/unified_pipeline/src/templates/template_provider.py
@@ -0,0 +1,110 @@
+"""Common structural interface both template sources satisfy.
+
+:class:`~src.templates.template_loader.TemplateLoader` (pre-made scanned
+document images + hand-measured JSON annotations) and
+:class:`~src.templates.procedural_template_provider.ProceduralTemplateProvider`
+(matplotlib-drawn documents, ported from the sibling ``projectA`` pipeline)
+already share this exact method shape without either importing the other --
+this :class:`typing.Protocol` documents that shared shape and lets
+:mod:`src.generator` type-hint against "either" without caring which.
+
+:class:`CompositeTemplateProvider` merges both into one id space for
+``paths.template_provider: both`` (see ``src/config/schema.py``).
+"""
+
+from __future__ import annotations
+
+from typing import Protocol, runtime_checkable
+
+import numpy as np
+
+from src.templates.template_loader import Template
+
+
+def template_kind(template_id: str) -> str:
+    """Which provider a template id came from, purely from its id prefix.
+
+    Used wherever downstream code (output folder naming, the dataset
+    summary CSV, the ground-truth JSON) needs to show "was this a scanned
+    photo or a procedurally-drawn document" without importing/instantiating
+    either provider -- template ids are already namespaced (`bank_doc_NN`
+    vs `proc_doc_NN`), so this is a plain string check.
+    """
+    if template_id.startswith("proc_doc_"):
+        return "procedural"
+    if template_id.startswith("bank_doc_"):
+        return "scanned"
+    return "unknown"
+
+
+@runtime_checkable
+class TemplateProvider(Protocol):
+    """Structural contract: anything with these four methods can supply templates."""
+
+    def discover(self) -> "dict[str, Template]": ...
+
+    def get(self, template_id: str) -> Template: ...
+
+    def list_ids(self) -> "list[str]": ...
+
+    def random_template(self, rng: "np.random.Generator") -> Template: ...
+
+
+class CompositeTemplateProvider:
+    """Merges several :class:`TemplateProvider`\\ s into one id space.
+
+    Each underlying provider already produces distinctly-prefixed ids
+    (``bank_doc_NN`` for scanned, ``proc_doc_NN`` for procedural), so no
+    collision handling is needed -- ``discover()`` just unions the dicts.
+    """
+
+    def __init__(self, providers: "list[TemplateProvider]") -> None:
+        if not providers:
+            raise ValueError("CompositeTemplateProvider needs at least one provider.")
+        self.providers = providers
+        self._templates: "dict[str, Template] | None" = None
+        self._owner: "dict[str, TemplateProvider] | None" = None
+
+    def discover(self) -> "dict[str, Template]":
+        if self._templates is not None:
+            return self._templates
+
+        templates: "dict[str, Template]" = {}
+        owner: "dict[str, TemplateProvider]" = {}
+        for provider in self.providers:
+            for template_id, template in provider.discover().items():
+                if template_id in templates:
+                    raise ValueError(
+                        f"Template id collision: '{template_id}' is produced by more than "
+                        f"one provider ({type(owner[template_id]).__name__} and {type(provider).__name__})."
+                    )
+                templates[template_id] = template
+                owner[template_id] = provider
+
+        self._templates = templates
+        self._owner = owner
+        return templates
+
+    def get(self, template_id: str) -> Template:
+        templates = self.discover()
+        if template_id not in templates:
+            raise KeyError(f"Unknown template id '{template_id}'. Known: {sorted(templates)}")
+        return templates[template_id]
+
+    def list_ids(self) -> "list[str]":
+        return sorted(self.discover().keys())
+
+    def random_template(self, rng: "np.random.Generator") -> Template:
+        ids = self.list_ids()
+        choice = ids[int(rng.integers(0, len(ids)))]
+        return self.get(choice)
+
+    def field_boxes_for(self, template_id: str) -> "dict | None":
+        """Delegates to the owning provider if it exposes this (only
+        :class:`~src.templates.procedural_template_provider.ProceduralTemplateProvider`
+        does); returns ``None`` for templates owned by a provider without it.
+        """
+        self.discover()
+        owner = self._owner.get(template_id)
+        fn = getattr(owner, "field_boxes_for", None)
+        return fn(template_id) if fn is not None else None
diff --git a/unified_pipeline/src/utils/__init__.py b/unified_pipeline/src/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..611013aa9bda467bc531c1bed90c1bfeee3192d1
--- /dev/null
+++ b/unified_pipeline/src/utils/__init__.py
@@ -0,0 +1 @@
+"""Shared utilities: logging, deterministic RNG management, geometry helpers, I/O, name generation."""
diff --git a/unified_pipeline/src/utils/geometry_utils.py b/unified_pipeline/src/utils/geometry_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..69f4fd9b6a23b0523344608155fcb0339a11f0e9
--- /dev/null
+++ b/unified_pipeline/src/utils/geometry_utils.py
@@ -0,0 +1,158 @@
+"""Geometry helper functions built on Shapely and NumPy.
+
+Centralizes every polygon/box computation used by placement and analysis so
+that the same, well-tested math backs both "where should the signature go"
+and "how well did it land" — avoiding subtle mismatches between the two.
+"""
+
+from __future__ import annotations
+
+from typing import Sequence
+
+import numpy as np
+from shapely.geometry import Polygon, box as shapely_box
+from shapely.validation import make_valid
+
+Box = tuple[float, float, float, float]  # x1, y1, x2, y2
+
+
+def box_to_polygon(box_coords: Box) -> Polygon:
+    """Convert an ``(x1, y1, x2, y2)`` axis-aligned box into a Shapely polygon."""
+    x1, y1, x2, y2 = box_coords
+    return shapely_box(min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2))
+
+
+def points_to_polygon(points: Sequence[Sequence[float]]) -> Polygon:
+    """Build a valid Shapely polygon from an ordered list of ``(x, y)`` points.
+
+    Degenerate inputs (fewer than 3 points, zero area) are expanded into a
+    minimal valid polygon by buffering, so downstream geometry ops never
+    raise on edge cases like a single-pixel detected signature.
+    """
+    pts = np.asarray(points, dtype=float)
+    if len(pts) < 3:
+        if len(pts) == 0:
+            return Polygon()
+        center = pts.mean(axis=0)
+        return shapely_box(center[0] - 0.5, center[1] - 0.5, center[0] + 0.5, center[1] + 0.5)
+    poly = Polygon(pts)
+    if not poly.is_valid:
+        poly = make_valid(poly)
+        if poly.geom_type != "Polygon":
+            poly = poly.convex_hull
+    if poly.area <= 0:
+        poly = poly.buffer(0.5)
+    return poly
+
+
+def safe_area(poly: Polygon) -> float:
+    """Return polygon area, guarding against ``None``/empty geometries."""
+    if poly is None or poly.is_empty:
+        return 0.0
+    return float(poly.area)
+
+
+def intersection_area(poly_a: Polygon, poly_b: Polygon) -> float:
+    """Return the intersection area of two polygons, 0 if either is empty/invalid."""
+    if poly_a is None or poly_b is None or poly_a.is_empty or poly_b.is_empty:
+        return 0.0
+    if not poly_a.is_valid:
+        poly_a = make_valid(poly_a)
+    if not poly_b.is_valid:
+        poly_b = make_valid(poly_b)
+    try:
+        return float(poly_a.intersection(poly_b).area)
+    except Exception:
+        return 0.0
+
+
+def iou(poly_a: Polygon, poly_b: Polygon) -> float:
+    """Intersection-over-Union of two polygons."""
+    inter = intersection_area(poly_a, poly_b)
+    union = safe_area(poly_a) + safe_area(poly_b) - inter
+    if union <= 0:
+        return 0.0
+    return float(np.clip(inter / union, 0.0, 1.0))
+
+
+def coverage_pct(target_box_poly: Polygon, signature_poly: Polygon) -> float:
+    """``100 * intersection(target_box, polygon) / area(target_box)``."""
+    target_area = safe_area(target_box_poly)
+    if target_area <= 0:
+        return 0.0
+    inter = intersection_area(target_box_poly, signature_poly)
+    return float(np.clip(100.0 * inter / target_area, 0.0, 100.0))
+
+
+def containment_pct(target_box_poly: Polygon, signature_poly: Polygon) -> float:
+    """``100 * intersection(target_box, polygon) / area(polygon)``."""
+    sig_area = safe_area(signature_poly)
+    if sig_area <= 0:
+        return 0.0
+    inter = intersection_area(target_box_poly, signature_poly)
+    return float(np.clip(100.0 * inter / sig_area, 0.0, 100.0))
+
+
+def centroid_of(poly: Polygon) -> tuple[float, float]:
+    """Return the ``(x, y)`` centroid of a polygon, ``(nan, nan)`` if empty."""
+    if poly is None or poly.is_empty:
+        return (float("nan"), float("nan"))
+    c = poly.centroid
+    return (float(c.x), float(c.y))
+
+
+def box_center(box_coords: Box) -> tuple[float, float]:
+    """Return the center point of an axis-aligned box."""
+    x1, y1, x2, y2 = box_coords
+    return ((x1 + x2) / 2.0, (y1 + y2) / 2.0)
+
+
+def box_size(box_coords: Box) -> tuple[float, float]:
+    """Return ``(width, height)`` of an axis-aligned box."""
+    x1, y1, x2, y2 = box_coords
+    return (abs(x2 - x1), abs(y2 - y1))
+
+
+def distance_between(p1: tuple[float, float], p2: tuple[float, float]) -> float:
+    """Euclidean distance between two points, ``nan``-safe."""
+    if any(np.isnan(v) for v in (*p1, *p2)):
+        return float("nan")
+    return float(np.hypot(p1[0] - p2[0], p1[1] - p2[1]))
+
+
+def margin_utilization(box_coords: Box, signature_poly: Polygon) -> float:
+    """Fraction of the box's available margin consumed by the signature (0..1+).
+
+    0 means the signature sits exactly at the box center with no spatial
+    extent; 1 means it exactly fills the box's half-diagonal. Values above 1
+    indicate the signature protrudes outside the box.
+    """
+    if signature_poly is None or signature_poly.is_empty:
+        return 0.0
+    bx, by = box_center(box_coords)
+    bw, bh = box_size(box_coords)
+    half_diag = float(np.hypot(bw, bh) / 2.0) or 1e-6
+    minx, miny, maxx, maxy = signature_poly.bounds
+    corners = [(minx, miny), (minx, maxy), (maxx, miny), (maxx, maxy)]
+    max_dist = max(distance_between((bx, by), c) for c in corners)
+    return float(max_dist / half_diag)
+
+
+def clamp01(value: float) -> float:
+    """Clamp a float into ``[0, 1]``, mapping NaN to 0."""
+    if value is None or np.isnan(value):
+        return 0.0
+    return float(np.clip(value, 0.0, 1.0))
+
+
+def rotated_rect_polygon(center: tuple[float, float], size: tuple[float, float], angle_deg: float) -> Polygon:
+    """Build a Shapely polygon for a rectangle rotated about its center (OpenCV convention)."""
+    cx, cy = center
+    w, h = size
+    theta = np.deg2rad(angle_deg)
+    dx = w / 2.0
+    dy = h / 2.0
+    corners = np.array([(-dx, -dy), (dx, -dy), (dx, dy), (-dx, dy)])
+    rot = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
+    rotated = corners @ rot.T + np.array([cx, cy])
+    return Polygon(rotated)
diff --git a/unified_pipeline/src/utils/image_io.py b/unified_pipeline/src/utils/image_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b14c56438fd25382f7e23d1d4133babfc6510bc
--- /dev/null
+++ b/unified_pipeline/src/utils/image_io.py
@@ -0,0 +1,62 @@
+"""Image I/O helpers built on Pillow + NumPy.
+
+Pillow is used for decoding (broad format support: PNG/JPG/JPEG/TIFF/BMP)
+while all pixel manipulation downstream is done with NumPy/OpenCV arrays in
+RGB(A) uint8 layout for consistency across the pipeline.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+SUPPORTED_IMAGE_EXTENSIONS: tuple[str, ...] = (
+    ".png", ".jpg", ".jpeg", ".tif", ".tiff", ".bmp",
+)
+
+
+def load_image_rgb(path: "Path | str") -> np.ndarray:
+    """Load an image file as an ``(H, W, 3)`` uint8 RGB array."""
+    with Image.open(path) as im:
+        return np.array(im.convert("RGB"))
+
+
+def load_image_any(path: "Path | str") -> np.ndarray:
+    """Load an image preserving its native mode (grayscale, RGB, or RGBA) as uint8.
+
+    Used for signature source images, which may be grayscale scans, palette
+    images, or already have an alpha channel.
+    """
+    with Image.open(path) as im:
+        if im.mode in ("L", "RGB", "RGBA"):
+            return np.array(im)
+        if im.mode == "LA":
+            return np.array(im.convert("RGBA"))
+        # Palette / CMYK / other -> normalize to RGB
+        return np.array(im.convert("RGB"))
+
+
+def save_image_rgb(path: "Path | str", array: np.ndarray) -> None:
+    """Save an ``(H, W, 3)`` or ``(H, W, 4)`` uint8 array as PNG/JPEG depending on extension."""
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    mode = "RGBA" if array.ndim == 3 and array.shape[2] == 4 else "RGB"
+    Image.fromarray(array.astype(np.uint8), mode=mode).save(path)
+
+
+def save_image_gray(path: "Path | str", array: np.ndarray) -> None:
+    """Save a single-channel uint8 array as a grayscale image."""
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    Image.fromarray(array.astype(np.uint8), mode="L").save(path)
+
+
+def discover_images(root: "Path | str", extensions: tuple[str, ...] = SUPPORTED_IMAGE_EXTENSIONS) -> list[Path]:
+    """Recursively discover image files under ``root``, sorted deterministically."""
+    root = Path(root)
+    if not root.exists():
+        return []
+    found = [p for p in root.rglob("*") if p.is_file() and p.suffix.lower() in extensions]
+    return sorted(found, key=lambda p: p.as_posix().lower())
diff --git a/unified_pipeline/src/utils/logging_utils.py b/unified_pipeline/src/utils/logging_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b42687b22b48bd41295cc46e168c8bca53cc3210
--- /dev/null
+++ b/unified_pipeline/src/utils/logging_utils.py
@@ -0,0 +1,53 @@
+"""Centralized logging configuration.
+
+The pipeline uses ``logging`` exclusively (no ``print``). Every module obtains
+its logger via :func:`get_logger`, which guarantees handlers are configured
+exactly once per process, even under multiprocessing worker re-imports.
+"""
+
+from __future__ import annotations
+
+import logging
+import sys
+from pathlib import Path
+
+_CONFIGURED = False
+
+LOG_FORMAT = "%(asctime)s | %(levelname)-8s | %(processName)s | %(name)s | %(message)s"
+DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
+
+
+def configure_logging(level: int = logging.INFO, log_file: "Path | None" = None) -> None:
+    """Configure the root logger once for the entire process.
+
+    Args:
+        level: Root logging level (e.g. ``logging.INFO``, ``logging.DEBUG``).
+        log_file: Optional file path to additionally write logs to.
+    """
+    global _CONFIGURED
+    root = logging.getLogger()
+    if _CONFIGURED:
+        root.setLevel(level)
+        return
+
+    root.setLevel(level)
+    formatter = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
+
+    stream_handler = logging.StreamHandler(stream=sys.stdout)
+    stream_handler.setFormatter(formatter)
+    root.addHandler(stream_handler)
+
+    if log_file is not None:
+        log_file.parent.mkdir(parents=True, exist_ok=True)
+        file_handler = logging.FileHandler(log_file, encoding="utf-8")
+        file_handler.setFormatter(formatter)
+        root.addHandler(file_handler)
+
+    _CONFIGURED = True
+
+
+def get_logger(name: str) -> logging.Logger:
+    """Return a module-scoped logger, configuring root logging with sane defaults if needed."""
+    if not _CONFIGURED:
+        configure_logging()
+    return logging.getLogger(name)
diff --git a/unified_pipeline/src/utils/names.py b/unified_pipeline/src/utils/names.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdab158f74b8761d6f97bbb9bf2970e5533313f6
--- /dev/null
+++ b/unified_pipeline/src/utils/names.py
@@ -0,0 +1,62 @@
+"""Deterministic synthetic signer name/role generation.
+
+No external dependency (e.g. Faker) is required: a compact, self-contained
+name pool keeps the pipeline dependency-light and fully reproducible from a
+seed alone (Faker's own generators are not guaranteed stable across
+versions, which would break the "same seed -> same output" contract).
+"""
+
+from __future__ import annotations
+
+import random
+
+FIRST_NAMES: tuple[str, ...] = (
+    "James", "Mary", "Robert", "Patricia", "John", "Jennifer", "Michael", "Linda",
+    "David", "Elizabeth", "William", "Barbara", "Richard", "Susan", "Joseph", "Jessica",
+    "Thomas", "Sarah", "Charles", "Karen", "Christopher", "Nancy", "Daniel", "Lisa",
+    "Matthew", "Betty", "Anthony", "Margaret", "Mark", "Sandra", "Donald", "Ashley",
+    "Steven", "Kimberly", "Paul", "Emily", "Andrew", "Donna", "Joshua", "Michelle",
+    "Kenneth", "Carol", "Kevin", "Amanda", "Brian", "Melissa", "George", "Deborah",
+    "Edward", "Stephanie", "Ronald", "Rebecca", "Timothy", "Laura", "Jason", "Sharon",
+    "Jeffrey", "Cynthia", "Ryan", "Kathleen", "Jacob", "Amy", "Gary", "Angela",
+    "Nicholas", "Shirley", "Eric", "Anna", "Jonathan", "Brenda", "Stephen", "Pamela",
+    "Larry", "Emma", "Justin", "Nicole", "Scott", "Helen", "Brandon", "Samantha",
+    "Wei", "Mei", "Hiroshi", "Yuki", "Raj", "Priya", "Ahmed", "Fatima",
+    "Carlos", "Sofia", "Hans", "Ingrid", "Liam", "Olivia", "Noah", "Ava",
+)
+
+LAST_NAMES: tuple[str, ...] = (
+    "Smith", "Johnson", "Williams", "Brown", "Jones", "Garcia", "Miller", "Davis",
+    "Rodriguez", "Martinez", "Hernandez", "Lopez", "Gonzalez", "Wilson", "Anderson", "Thomas",
+    "Taylor", "Moore", "Jackson", "Martin", "Lee", "Perez", "Thompson", "White",
+    "Harris", "Sanchez", "Clark", "Ramirez", "Lewis", "Robinson", "Walker", "Young",
+    "Allen", "King", "Wright", "Scott", "Torres", "Nguyen", "Hill", "Flores",
+    "Green", "Adams", "Nelson", "Baker", "Hall", "Rivera", "Campbell", "Mitchell",
+    "Carter", "Roberts", "Chen", "Tan", "Kumar", "Patel", "Khan", "Ali",
+    "Muller", "Schmidt", "Rossi", "Dubois", "Kowalski", "Ivanov", "Suzuki", "Tanaka",
+)
+
+ROLE_POOL: tuple[str, ...] = (
+    "Account Holder",
+    "Authorized Signatory",
+    "Branch Manager",
+    "Bank Representative",
+    "Bank Officer",
+    "Loan Officer",
+    "Notary Public",
+    "Guarantor",
+    "Co-Signer",
+    "Compliance Officer",
+    "First Authorised Signatory",
+    "Second Authorised Signatory",
+)
+
+
+def generate_name(rng: random.Random) -> str:
+    """Generate a deterministic synthetic full name from a seeded RNG."""
+    return f"{rng.choice(FIRST_NAMES)} {rng.choice(LAST_NAMES)}"
+
+
+def generate_role(rng: random.Random) -> str:
+    """Generate a deterministic synthetic role from a seeded RNG."""
+    return rng.choice(ROLE_POOL)
diff --git a/unified_pipeline/src/utils/random_utils.py b/unified_pipeline/src/utils/random_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec49d094baf07e168ce757ab231defdff5f0cad9
--- /dev/null
+++ b/unified_pipeline/src/utils/random_utils.py
@@ -0,0 +1,96 @@
+"""Deterministic random-state management.
+
+The generator must produce byte-identical output for a given ``(master_seed,
+sample_index)`` pair regardless of process/thread scheduling, batch size, or
+worker count. To guarantee this we never share a single global RNG across
+samples or pipeline stages. Instead, every stage of every sample draws from
+an independently *derived* RNG whose seed is a pure function of
+``(master_seed, sample_index, stage_name)``.
+
+This makes the pipeline safe for multiprocessing: worker N generating sample
+57 produces exactly the same random draws as a single-process run would have,
+because nothing depends on execution order.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import random
+from typing import Any
+
+import numpy as np
+
+_MOD32 = 2**32
+
+
+def derive_seed(master_seed: int, *parts: Any) -> int:
+    """Derive a deterministic 32-bit seed from a master seed and arbitrary parts.
+
+    Uses SHA-256 over the concatenated string representation of all parts so
+    that the mapping is stable across Python versions and processes (unlike
+    Python's salted ``hash()``).
+
+    Args:
+        master_seed: The top-level seed configured by the user.
+        *parts: Any number of hashable/stringifiable disambiguators, e.g.
+            sample index and pipeline stage name.
+
+    Returns:
+        A non-negative integer in ``[0, 2**32)`` suitable for seeding
+        ``numpy.random.Generator`` or ``random.Random``.
+    """
+    key = f"{master_seed}|" + "|".join(str(p) for p in parts)
+    digest = hashlib.sha256(key.encode("utf-8")).digest()
+    return int.from_bytes(digest[:4], byteorder="big") % _MOD32
+
+
+class SeedManager:
+    """Provides independent, deterministic RNGs for each pipeline stage of one sample.
+
+    Example:
+        >>> sm = SeedManager(master_seed=42, sample_index=7)
+        >>> rng = sm.numpy_rng("placement")
+        >>> rng.uniform(0, 1)
+    """
+
+    def __init__(self, master_seed: int, sample_index: int) -> None:
+        self.master_seed = master_seed
+        self.sample_index = sample_index
+
+    def seed_for(self, stage: str) -> int:
+        """Return the deterministic integer seed for a named pipeline stage."""
+        return derive_seed(self.master_seed, self.sample_index, stage)
+
+    def numpy_rng(self, stage: str) -> np.random.Generator:
+        """Return a fresh ``numpy.random.Generator`` (PCG64) seeded for ``stage``."""
+        return np.random.default_rng(self.seed_for(stage))
+
+    def python_rng(self, stage: str) -> random.Random:
+        """Return a fresh ``random.Random`` seeded for ``stage``."""
+        return random.Random(self.seed_for(stage))
+
+
+def resolve_scalar_or_range(value: Any, rng: np.random.Generator) -> float:
+    """Resolve a config parameter that is either a fixed scalar or a ``{min, max}`` range.
+
+    Args:
+        value: Either a plain number, or an object/mapping exposing ``min``
+            and ``max`` attributes/keys (a :class:`~src.config.schema.Range`
+            or an equivalent dict).
+        rng: The seeded generator to draw from when ``value`` is a range.
+
+    Returns:
+        A resolved float value.
+    """
+    if isinstance(value, (int, float)):
+        return float(value)
+    if isinstance(value, dict) and "min" in value and "max" in value:
+        lo, hi = float(value["min"]), float(value["max"])
+        return float(rng.uniform(lo, hi)) if hi > lo else lo
+    # Pydantic Range model (duck-typed access)
+    lo = getattr(value, "min", None)
+    hi = getattr(value, "max", None)
+    if lo is not None and hi is not None:
+        lo, hi = float(lo), float(hi)
+        return float(rng.uniform(lo, hi)) if hi > lo else lo
+    raise TypeError(f"Cannot resolve scalar-or-range value: {value!r}")
diff --git a/unified_pipeline/src/utils/transform_utils.py b/unified_pipeline/src/utils/transform_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7d35d9175e1ee303a54aaea4f4bee2be2198274
--- /dev/null
+++ b/unified_pipeline/src/utils/transform_utils.py
@@ -0,0 +1,49 @@
+"""Shared geometric raster transforms used by both placement and augmentation."""
+
+from __future__ import annotations
+
+import cv2
+import numpy as np
+
+
+def rotate_and_scale_rgba(rgba: np.ndarray, angle_deg: float, scale: float = 1.0) -> np.ndarray:
+    """Rotate (and optionally scale) an RGBA image about its own center.
+
+    The output canvas automatically expands to tightly bound the rotated
+    rectangle so no content is clipped; newly exposed corners are fully
+    transparent.
+    """
+    h, w = rgba.shape[:2]
+    if h == 0 or w == 0:
+        return rgba
+    cx, cy = w / 2.0, h / 2.0
+    matrix = cv2.getRotationMatrix2D((cx, cy), angle_deg, scale)
+    cos, sin = abs(matrix[0, 0]), abs(matrix[0, 1])
+    new_w = max(int(np.ceil(h * sin + w * cos)), 1)
+    new_h = max(int(np.ceil(h * cos + w * sin)), 1)
+    matrix[0, 2] += new_w / 2.0 - cx
+    matrix[1, 2] += new_h / 2.0 - cy
+    return cv2.warpAffine(
+        rgba, matrix, (new_w, new_h),
+        flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0),
+    )
+
+
+def smooth_noise_field(shape: tuple[int, int], rng: np.random.Generator, blur_frac: float = 0.08) -> np.ndarray:
+    """Generate a smooth, low-frequency noise field in ``[0, 1]`` of the given ``(h, w)`` shape.
+
+    Built by upsampling coarse random noise, which is a fast and dependency-free
+    stand-in for Perlin/simplex noise -- adequate for subtle shading effects
+    like paper texture, discoloration, and ink fading.
+    """
+    h, w = shape
+    coarse_h = max(int(h * blur_frac), 4)
+    coarse_w = max(int(w * blur_frac), 4)
+    coarse = rng.uniform(0.0, 1.0, size=(coarse_h, coarse_w)).astype(np.float32)
+    field = cv2.resize(coarse, (w, h), interpolation=cv2.INTER_CUBIC)
+    field = cv2.GaussianBlur(field, (0, 0), sigmaX=max(w, h) * 0.01 + 1)
+    field -= field.min()
+    max_val = field.max()
+    if max_val > 1e-6:
+        field /= max_val
+    return field
diff --git a/unified_pipeline/src/verification/__init__.py b/unified_pipeline/src/verification/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/unified_pipeline/src/verification/case_types.py b/unified_pipeline/src/verification/case_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a7e0beac74a940f00deb7f22067fca3dc5200e2
--- /dev/null
+++ b/unified_pipeline/src/verification/case_types.py
@@ -0,0 +1,55 @@
+"""The verification test-case taxonomy: ``case_type`` -> ground-truth verdict.
+
+Each :class:`CaseSpec` encodes one row of the signature-verification test
+plan: what should actually be rendered into the target signature box
+(``signature_mode``), whether a companion box on the same page should also
+be filled with its own correctly-authorized signature (``fill_companion`` --
+only meaningful for the multi-signer-context cases), and the ground-truth
+``verdict``/``reject_code`` a downstream verifier is expected to produce.
+
+Driven by the existing ``quality.case_type`` config field (see
+``configs/examples/hard_degraded.yaml`` for its prior, simpler use as a
+plain metadata label) -- setting it to one of the codes below switches
+:meth:`~src.generator.GeneratorContext.run_sample` into scenario-driven
+generation. Any other value (or ``None``) leaves generation entirely
+unaffected.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+SignatureMode = str  # one of the _SIGNATURE_MODES below, kept as a plain str for simplicity
+
+NO_SIGNATURE = "NO_SIGNATURE"
+IDENTITY_MISMATCH = "IDENTITY_MISMATCH"
+ROLE_MISMATCH = "ROLE_MISMATCH"
+REQUIRED_SIGNER_ABSENT = "REQUIRED_SIGNER_ABSENT"
+
+
+@dataclass(frozen=True)
+class CaseSpec:
+    """One row of the verification test-case taxonomy."""
+
+    code: str
+    description: str
+    verdict: str  # "PASS" | "REJECT"
+    reject_code: "str | None"
+    signature_mode: SignatureMode
+    fill_companion: bool = False
+
+
+CASE_TABLE: dict[str, CaseSpec] = {
+    "1a": CaseSpec("1a", "Correct signer signs normally", "PASS", None, "genuine_correct"),
+    "1b": CaseSpec("1b", "Correct signer, natural pen variation applied", "PASS", None, "genuine_variation"),
+    "1c": CaseSpec("1c", "Multi-signer form, both sign correctly", "PASS", None, "genuine_correct", fill_companion=True),
+    "1d": CaseSpec("1d", "Correct signer, scan degraded to test robustness", "PASS", None, "genuine_correct"),
+    "2a": CaseSpec("2a", "Box left completely blank", "REJECT", NO_SIGNATURE, "blank"),
+    "2b": CaseSpec("2b", "Printed/typed name only, no handwriting", "REJECT", NO_SIGNATURE, "printed_text"),
+    "3a": CaseSpec("3a", "Wrong real person signs the box", "REJECT", IDENTITY_MISMATCH, "wrong_signer_genuine"),
+    "3b": CaseSpec("3b", "Skilled-forgery sample used", "REJECT", IDENTITY_MISMATCH, "forged_skilled"),
+    "4a": CaseSpec("4a", "Authorized signer, but wrong role's box", "REJECT", ROLE_MISMATCH, "role_swap", fill_companion=True),
+    "4b": CaseSpec("4b", "Signer from a different form entirely", "REJECT", ROLE_MISMATCH, "foreign_template_signer", fill_companion=True),
+    "5a": CaseSpec("5a", "Required signer's box left empty", "REJECT", REQUIRED_SIGNER_ABSENT, "blank", fill_companion=True),
+    "5b": CaseSpec("5b", "Someone else signs in the required signer's place", "REJECT", REQUIRED_SIGNER_ABSENT, "wrong_signer_genuine", fill_companion=True),
+}
diff --git a/unified_pipeline/src/verification/registry.py b/unified_pipeline/src/verification/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bc5b22178b442bc863185d9a05d3e922ea3f0c8
--- /dev/null
+++ b/unified_pipeline/src/verification/registry.py
@@ -0,0 +1,99 @@
+"""Deterministic authorized-signer identity registry.
+
+The base generation pipeline has no notion of "the correct signer for this
+box" -- it just draws a random genuine signature for whichever box was
+picked. Every verification case type (see :mod:`src.verification.case_types`)
+hinges on comparing an *authorized* identity against an *actual* one, so this
+module derives that ground truth purely from ``(master_seed, template_id,
+box_id)`` via :func:`~src.utils.random_utils.derive_seed` -- no persisted
+state, fully reproducible, consistent with the rest of the pipeline's
+seeding philosophy.
+"""
+
+from __future__ import annotations
+
+import random
+import re
+
+from src.signatures.signature_dataset import SignatureDataset
+from src.templates.template_loader import TemplateLoader
+from src.utils.names import generate_name
+from src.utils.random_utils import derive_seed
+
+# Signer groups auto-derived by SignatureDataset (CEDAR filenames, or no
+# metadata.csv identity override; also the SigNet/BHSig260/GPDS sources
+# added by the unified-pipeline merge -- see
+# src/signatures/signature_dataset.py's _discover_signet/_discover_bhsig260/
+# _discover_gpds) look like "signer_0014"/"signet_0049"/"bhsig260_hindi_0003"/
+# "gpds_0012" and carry no real name -- those get a generated display name.
+# Groups sourced from metadata.csv (e.g. "Priya Sharma") already *are* a
+# real name and must NOT match here.
+_AUTO_GROUP_PATTERN = re.compile(r"^(signer|signet|bhsig260_hindi|bhsig260_bengali|gpds)_\d+$")
+
+
+class SignerIdentityRegistry:
+    """Resolves ground-truth authorized signer identities for template boxes.
+
+    Example:
+        >>> registry = SignerIdentityRegistry(template_loader, signature_dataset, master_seed=1234)
+        >>> registry.authorized_signer_group("bank_doc_03", "sig1")
+        'signer_0014'
+    """
+
+    def __init__(self, template_loader: TemplateLoader, signature_dataset: SignatureDataset, master_seed: int) -> None:
+        self.template_loader = template_loader
+        self.signature_dataset = signature_dataset
+        self.master_seed = master_seed
+        self._signer_groups = signature_dataset.signer_groups()
+
+    def authorized_signer_group(self, template_id: str, box_id: str) -> str:
+        """Return the deterministic ground-truth authorized signer for one (template, box)."""
+        seed = derive_seed(self.master_seed, template_id, box_id, "authorized_signer")
+        idx = seed % len(self._signer_groups)
+        return self._signer_groups[idx]
+
+    def foreign_authorized(self, exclude_template_id: str) -> tuple[str, str, str]:
+        """Return ``(signer_group, template_id, box_id)`` for a box on a *different* template.
+
+        Used for the "signer from a different form entirely" scenario: the
+        identity is a legitimately authorized signer, just not for this
+        document.
+        """
+        templates = self.template_loader.discover()
+        candidate_ids = sorted(tid for tid in templates if tid != exclude_template_id)
+        if not candidate_ids:
+            # Only one template discovered; fall back to a different box on the same one.
+            candidate_ids = [exclude_template_id]
+
+        seed = derive_seed(self.master_seed, exclude_template_id, "foreign_template")
+        foreign_template_id = candidate_ids[seed % len(candidate_ids)]
+
+        boxes = templates[foreign_template_id].annotation.pages[0].signature_boxes
+        box_seed = derive_seed(self.master_seed, exclude_template_id, foreign_template_id, "foreign_box")
+        box = boxes[box_seed % len(boxes)]
+
+        return self.authorized_signer_group(foreign_template_id, box.id), foreign_template_id, box.id
+
+    def random_wrong_signer_group(self, rng: random.Random, exclude_group: str) -> str:
+        """Return a uniformly random signer group other than ``exclude_group``."""
+        pool = [g for g in self._signer_groups if g != exclude_group]
+        return rng.choice(pool)
+
+    def display_name(self, signer_group: str) -> str:
+        """Return a stable, human-readable display name for a signer group.
+
+        Groups sourced from ``dataset/metadata.csv`` (e.g. ``"Priya Sharma"``)
+        already carry a real name and are returned as-is. Anonymous
+        auto-derived groups (e.g. ``"signer_0014"``, from the bundled CEDAR
+        dataset) get a generated name, stable across an entire run so a
+        benchmark built from many samples reads as consistent identities
+        rather than a fresh random name every time.
+        """
+        if not _AUTO_GROUP_PATTERN.match(signer_group):
+            return signer_group
+        seed = derive_seed(self.master_seed, signer_group, "display_name")
+        return generate_name(random.Random(seed))
+
+    def script_for(self, signer_group: str) -> str:
+        """Return the handwriting script (e.g. ``"latin"``, ``"hindi"``, ``"bengali"``) for a signer group."""
+        return self.signature_dataset.script_for_group(signer_group)
diff --git a/unified_pipeline/src/visualization/__init__.py b/unified_pipeline/src/visualization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c08cea506dc0adabeafb168885418ed360595601
--- /dev/null
+++ b/unified_pipeline/src/visualization/__init__.py
@@ -0,0 +1 @@
+"""Debug visualization figures for generated samples."""
diff --git a/unified_pipeline/src/visualization/before_after_preview.py b/unified_pipeline/src/visualization/before_after_preview.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb57d781efabcb06339f4484ea64b319ab652ed0
--- /dev/null
+++ b/unified_pipeline/src/visualization/before_after_preview.py
@@ -0,0 +1,341 @@
+"""Before/after degradation comparison -- ported from projectA's
+``metrics_overlay.py::build_before_after_preview()``/``render_metrics_overlay()``.
+
+Side by side: the clean render (signature placed and blended, before any
+document-level augmentation -- stains/tilt/blur/noise/etc.), annotated with
+the same "what was actually measured" overlay projectA drew (target box,
+actual signature bbox, ink polygon, cosmetic field boxes on the image;
+their color legend, exact numbers, a difficulty-score breakdown, and a
+list of which augmentation effects actually fired in an info panel below
+-- see ``_render_info_panel``'s docstring for why numbers/legend live
+there and not as text floating on the image itself) -- next to the final
+look (after whatever augmentation actually fired for this sample). Answers
+both "was this measured correctly" and "what did degradation actually do
+to this document" at a glance -- distinct from
+:func:`~src.visualization.visualizer.save_visualization`'s single-image,
+matplotlib-based multi-panel figure (crop + mask + bar chart), which is
+heavier and only shows the final image, not a before/after.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+_OVERLAY_COLORS = {
+    "target_box": (220, 40, 40),
+    "signature_bbox": (230, 200, 30),
+    "polygon": (60, 220, 90),
+    "field_box": (40, 170, 230),
+    "field_target_box": (240, 140, 20),
+}
+
+# A second, visually distinct palette for OTHER signed boxes on a
+# multi-box template (companion / additional_signatures) -- these are
+# genuinely rendered with a real signature but aren't this sample's SCORED
+# target (geometry/difficulty stay keyed to the one target box), so
+# without some overlay a reviewer sees one scored signature and one
+# unexplained, unlabeled scribble elsewhere on the page.
+_EXTRA_OVERLAY_COLORS = {
+    "target_box": (150, 60, 200),
+    "signature_bbox": (200, 150, 255),
+    "polygon": (255, 120, 220),
+}
+
+# (label, weight_attr, component_key) -- display order matches
+# projectA's DIFFICULTY BREAKDOWN panel and configs/default.yaml's
+# difficulty: section.
+_DIFFICULTY_COMPONENTS = [
+    ("misplacement", "misplacement"),
+    ("whitespace", "whitespace"),
+    ("faintness", "faintness"),
+    ("smallness", "smallness"),
+    ("low_texture", "low_texture"),
+]
+
+
+def _get_font(size: int = 14, bold: bool = False) -> "ImageFont.FreeTypeFont":
+    candidates = [
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf" if bold else "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+        "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf",
+        "/Library/Fonts/Arial Bold.ttf" if bold else "/Library/Fonts/Arial.ttf",
+        "C:\\Windows\\Fonts\\arialbd.ttf" if bold else "C:\\Windows\\Fonts\\arial.ttf",
+        "arialbd.ttf" if bold else "arial.ttf",
+        "DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf",
+    ]
+    for path in candidates:
+        try:
+            return ImageFont.truetype(path, size)
+        except Exception:
+            continue
+    try:
+        return ImageFont.load_default(size=size)
+    except TypeError:
+        return ImageFont.load_default()
+
+
+def _label_banner(image: "np.ndarray | Image.Image", text: str, bg_color=(30, 41, 59), text_color=(255, 255, 255)) -> Image.Image:
+    """A small labeled title bar across the top of a copy of ``image``."""
+    labeled = (image if isinstance(image, Image.Image) else Image.fromarray(image.astype(np.uint8))).convert("RGB")
+    draw = ImageDraw.Draw(labeled)
+    font = _get_font(20, bold=True)
+    draw.rectangle([0, 0, labeled.width, 34], fill=bg_color)
+    draw.text((10, 6), text, fill=text_color, font=font)
+    return labeled
+
+
+def _applied_effects_summary(metadata: "dict[str, Any]") -> str:
+    effects = (metadata.get("augmentation") or {}).get("effects") or {}
+    applied = sorted(name for name, rec in effects.items() if rec.get("applied"))
+    if not applied:
+        return "no augmentation effects applied"
+    return f"{len(applied)} effect(s): " + ", ".join(applied)
+
+
+def _fill_polygon(fill_draw: "ImageDraw.ImageDraw", geometry: "dict[str, Any]", colors: "dict[str, tuple]") -> None:
+    """Tints the actual ink -- and only the actual ink -- with a
+    translucent fill. `signature_polygon_px` (the convex hull, used for
+    scoring, matches projectA's own computation including its
+    approxPolyDP simplification) "bridges" gaps between separate ink blobs
+    (e.g. between letters, or a flourish far from the main strokes) with
+    straight lines -- correct for coverage/IoU math, but on direct user
+    feedback, confirmed to still visibly fail to "hug" a signature with a
+    real gap even when rendered exactly the way projectA renders it (an
+    outline, no fill) -- that's projectA's own known tradeoff, just not
+    visible on a compact signature with no internal gap.
+
+    `display_contours` (real per-ink-blob boundaries -- see the field's
+    docstring on GeometryResult) fixes this: each disjoint blob gets its
+    own fill, so the tint only ever lands on actual ink. An *outline* of
+    these same per-blob contours was tried once and rejected -- for a
+    multi-letter cursive/Devanagari signature, the dense scribbly outline
+    itself visually resembled a second overlaid signature. A translucent
+    *fill* has no separate line art to be confused with handwriting, just
+    a soft wash exactly where the ink already is. Falls back to filling
+    the hull for any geometry dict that predates this field.
+    """
+    contours = geometry.get("display_contours")
+    if contours:
+        for contour in contours:
+            if len(contour) >= 3:
+                fill_draw.polygon([tuple(p) for p in contour], fill=(*colors["polygon"], 90))
+        return
+    polygon = geometry.get("signature_polygon_px") or []
+    if len(polygon) >= 3:
+        fill_draw.polygon([tuple(p) for p in polygon], fill=(*colors["polygon"], 70), outline=(*colors["polygon"], 200))
+
+
+def _draw_box_outlines(draw: "ImageDraw.ImageDraw", geometry: "dict[str, Any]", colors: "dict[str, tuple]") -> None:
+    """Draws one box's target rectangle and detected signature bbox --
+    shared by the primary (scored) box and any extra (companion/additional,
+    not scored) boxes, just with different colors. No floating text on the
+    image itself (see ``_render_info_panel``'s SIGNATURE BOXES section for
+    the numbers): two boxes close together (e.g. side-by-side
+    Borrower/Lender lines) used to get their metric-text labels drawn
+    right on top of each other, and a legend pinned to a fixed corner
+    collided with real document text on wide multi-page-spread scans.
+    Both were real bugs, not intentional -- text in a dedicated panel
+    can't collide with the document OR with another box's numbers,
+    regardless of page layout."""
+    tb = geometry.get("target_box")
+    ab = geometry.get("actual_box")
+    if tb:
+        draw.rectangle(list(tb), outline=colors["target_box"], width=3)
+    if ab:
+        draw.rectangle(list(ab), outline=colors["signature_bbox"], width=2)
+
+
+def _draw_metric_overlays(
+    clean_image: "np.ndarray", metadata: "dict[str, Any]", extra_boxes: "list[dict] | None" = None,
+) -> Image.Image:
+    """Draws the target box, the actual placed signature bbox, its detected
+    ink polygon (as a translucent fill of the real ink blob(s) -- see
+    ``_fill_polygon``'s docstring), and any cosmetic field boxes onto a
+    copy of ``clean_image`` -- the same "what was actually measured"
+    overlay as projectA's ``render_metrics_overlay()``. This pipeline
+    scores exactly one target box per sample (unlike projectA's per-box
+    list), but a multi-box template can have OTHER boxes genuinely signed
+    too (companion / additional_signatures) -- ``extra_boxes`` (see
+    :func:`src.generator.compute_extra_signature_overlays`) draws those
+    too, in a visually distinct color. The actual numbers/legend live in
+    the info panel below, not as text overlaid on the image -- see
+    ``_draw_box_outlines``'s docstring for why."""
+    overlay = Image.fromarray(clean_image.astype(np.uint8)).convert("RGB")
+
+    # Fills drawn on a separate transparent layer, then alpha-composited
+    # onto the image once -- PIL's ImageDraw has no direct way to paint a
+    # translucent shape onto an opaque RGB image.
+    fill_layer = Image.new("RGBA", overlay.size, (0, 0, 0, 0))
+    fill_draw = ImageDraw.Draw(fill_layer)
+
+    geometry = metadata.get("geometry") or {}
+    _fill_polygon(fill_draw, geometry, _OVERLAY_COLORS)
+    for extra in extra_boxes or []:
+        _fill_polygon(fill_draw, extra.get("geometry") or {}, _EXTRA_OVERLAY_COLORS)
+
+    overlay = Image.alpha_composite(overlay.convert("RGBA"), fill_layer).convert("RGB")
+    draw = ImageDraw.Draw(overlay)
+
+    _draw_box_outlines(draw, geometry, _OVERLAY_COLORS)
+    for extra in extra_boxes or []:
+        _draw_box_outlines(draw, extra.get("geometry") or {}, _EXTRA_OVERLAY_COLORS)
+
+    for field_name, boxes in ((metadata.get("cosmetics") or {}).get("field_boxes") or {}).items():
+        target_box = boxes.get("target_box") if isinstance(boxes, dict) else None
+        actual_box = boxes.get("actual_box") if isinstance(boxes, dict) else boxes
+        if target_box:
+            x1, y1, x2, y2 = target_box
+            draw.rectangle([x1 + 2, y1 + 2, x2 - 2, y2 - 2], outline=_OVERLAY_COLORS["field_target_box"], width=1)
+        if actual_box:
+            draw.rectangle(list(actual_box), outline=_OVERLAY_COLORS["field_box"], width=1)
+
+    return overlay
+
+
+def _metrics_line(metrics: "dict[str, Any]") -> str:
+    return (
+        f"cov {metrics.get('box_coverage_pct', 0):.1f}% | contain {metrics.get('containment_pct', 0):.1f}% | "
+        f"IoU {metrics.get('polygon_overlap_proxy', 0):.3f} | ink_fill {metrics.get('ink_fill_ratio', 0):.3f} | "
+        f"dark {metrics.get('mean_dark_intensity', 0):.1f} | margin {metrics.get('margin_utilization', 0):.3f}"
+    )
+
+
+def _render_info_panel(
+    width: int, metadata: "dict[str, Any]", difficulty_weights: "Any | None", extra_boxes: "list[dict] | None" = None,
+) -> Image.Image:
+    """Bottom info panel, appended below the overlaid clean image: a
+    SIGNATURE BOXES section (color legend + exact numbers for the target
+    box and any extra signed boxes -- see below for why these live here
+    and not as floating text on the image), a DIFFICULTY BREAKDOWN section
+    (what built the score -- each weighted component), and an AUGMENTATION
+    APPLIED section (every effect this sample rolled, on/off and at what
+    strength) -- the same two latter sections as projectA's
+    ``_render_breakdown_and_degradation_panel()``.
+
+    Numbers and the color legend live here, in a plain vertically-stacked
+    list, rather than as text floating above each box on the document
+    image itself (the original design, ported from projectA) -- two boxes
+    close together (e.g. side-by-side Borrower/Lender signature lines, or
+    a signature box near its own cosmetic "Print Name:" field) drew their
+    text right on top of each other, and a legend pinned to a fixed image
+    corner collided with real document text on wide multi-page-spread
+    scans. A dedicated panel can't collide with either, regardless of the
+    document's own layout.
+    """
+    line_h = 17
+    pad = 10
+    swatch = 12
+    header_font = _get_font(14, bold=True)
+    body_font = _get_font(12)
+
+    difficulty = metadata.get("difficulty") or {}
+    effects = (metadata.get("augmentation") or {}).get("effects") or {}
+    extra_boxes = extra_boxes or []
+
+    # (color_or_None, text, text_color) -- color_or_None draws a small
+    # swatch to the left of the line; None means no swatch (plain text).
+    lines: "list[tuple[tuple | None, str, tuple]]" = []
+
+    lines.append((None, "SIGNATURE BOXES -- what was measured", (20, 20, 20)))
+    lines.append((_OVERLAY_COLORS["target_box"], "target (scored)", (20, 20, 20)))
+    lines.append((None, "  " + _metrics_line(metadata.get("metrics") or {}), (20, 20, 20)))
+    for extra in extra_boxes:
+        lines.append((_EXTRA_OVERLAY_COLORS["target_box"], f"{extra.get('label') or 'other signed box'} (not scored)", (20, 20, 20)))
+        metrics = extra.get("metrics")
+        lines.append((None, "  " + (_metrics_line(metrics) if metrics else "no ink detected"), (20, 20, 20)))
+    if (metadata.get("cosmetics") or {}).get("field_boxes"):
+        lines.append((_OVERLAY_COLORS["field_box"], "cosmetic field box (actual)", (20, 20, 20)))
+        lines.append((_OVERLAY_COLORS["field_target_box"], "cosmetic field box (target cell)", (20, 20, 20)))
+
+    lines.append((None, "", (0, 0, 0)))  # spacer
+    lines.append((None, "DIFFICULTY BREAKDOWN -- what built the score", (20, 20, 20)))
+    for label, key in _DIFFICULTY_COMPONENTS:
+        value = difficulty.get(key)
+        if value is None:
+            continue
+        weight = getattr(difficulty_weights, key, None) if difficulty_weights is not None else None
+        if weight is not None:
+            lines.append((None, f"  {label}: {value:.3f} x {weight:.2f} = {value * weight:.3f}", (20, 20, 20)))
+        else:
+            lines.append((None, f"  {label}: {value:.3f}", (20, 20, 20)))
+    lines.append((None, f"difficulty_score = {difficulty.get('score')} -> {str(difficulty.get('tier')).upper()}", (20, 20, 20)))
+
+    lines.append((None, "", (0, 0, 0)))  # spacer
+    lines.append((None, "AUGMENTATION APPLIED -- document/signature-layer effects", (20, 20, 20)))
+    if not effects:
+        lines.append((None, "  (no per-effect record in this sample's metadata)", (90, 90, 90)))
+    for name in sorted(effects):
+        rec = effects[name] or {}
+        if rec.get("applied"):
+            lines.append((None, f"  {name}: ON (strength={rec.get('strength')})", (20, 20, 20)))
+        else:
+            lines.append((None, f"  {name}: off", (120, 120, 120)))
+
+    height = pad * 2 + len(lines) * line_h + 10
+    panel = Image.new("RGB", (width, height), (245, 246, 248))
+    draw = ImageDraw.Draw(panel)
+    y = pad
+    header_texts = {"SIGNATURE BOXES -- what was measured", "DIFFICULTY BREAKDOWN -- what built the score", "AUGMENTATION APPLIED -- document/signature-layer effects"}
+    for color, text, text_color in lines:
+        font = header_font if text in header_texts else body_font
+        text_x = pad
+        if color is not None:
+            draw.rectangle([pad, y + 2, pad + swatch, y + 2 + swatch], outline=color, width=2)
+            text_x = pad + swatch + 8
+        if text:
+            draw.text((text_x, y), text, fill=text_color, font=font)
+        y += line_h
+
+    return panel
+
+
+def save_before_after_preview(
+    path: "str | Path", clean_image: "np.ndarray", final_image: "np.ndarray", metadata: "dict[str, Any]",
+    difficulty_weights: "Any | None" = None, extra_boxes: "list[dict] | None" = None,
+) -> None:
+    """Save a side-by-side clean-vs-final comparison PNG.
+
+    Args:
+        path: Output PNG path.
+        clean_image: The document right after signature placement/blend,
+            before any document-level augmentation.
+        final_image: The fully augmented, final rendered document.
+        metadata: The sample's full metadata dict (used to draw the metric
+            overlays/legend/difficulty-breakdown/augmentation-applied panel
+            on the BEFORE panel, and to label the AFTER panel with which/how
+            many effects actually fired).
+        difficulty_weights: Optional ``cfg.difficulty`` (a
+            :class:`~src.config.schema.DifficultyWeights`) -- when given,
+            the difficulty breakdown shows each component's actual weighted
+            contribution (``value x weight = contribution``), matching the
+            run's real config instead of just the raw component values.
+        extra_boxes: Optional list from
+            :func:`src.generator.compute_extra_signature_overlays` -- every
+            OTHER signed box on this sample's template (companion /
+            additional_signatures), drawn in a distinct color so a
+            multi-box template's second signature isn't left unexplained
+            on the preview.
+    """
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    overlaid = _draw_metric_overlays(clean_image, metadata, extra_boxes)
+    panel = _render_info_panel(overlaid.width, metadata, difficulty_weights, extra_boxes)
+    left_combined = Image.new("RGB", (overlaid.width, overlaid.height + panel.height), (255, 255, 255))
+    left_combined.paste(overlaid, (0, 0))
+    left_combined.paste(panel, (0, overlaid.height))
+
+    left = _label_banner(left_combined, "BEFORE - metrics (measured here, pre-degradation)")
+    right = _label_banner(final_image, f"AFTER - {_applied_effects_summary(metadata)}")
+
+    combined = Image.new("RGB", (left.width + right.width + 10, max(left.height, right.height)), (255, 255, 255))
+    combined.paste(left, (0, 0))
+    combined.paste(right, (left.width + 10, 0))
+    combined.save(path)
diff --git a/unified_pipeline/src/visualization/visualizer.py b/unified_pipeline/src/visualization/visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bb4509eced5f07285fcf4c5b9aad441ebeceb48
--- /dev/null
+++ b/unified_pipeline/src/visualization/visualizer.py
@@ -0,0 +1,149 @@
+"""Renders a multi-panel debug figure for one generated sample.
+
+Shows the full rendered document with the target box, detected bounding
+box, and detected polygon overlaid; a crop of the signature field; a mask
+reconstructed from the detected ink polygon; the resolved coverage metrics;
+and a difficulty-component breakdown bar chart.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import cv2
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+from src.utils.logging_utils import get_logger
+
+logger = get_logger(__name__)
+
+
+def _draw_overlays(image: np.ndarray, metadata: dict[str, Any]) -> np.ndarray:
+    out = image.copy()
+    geometry = metadata["geometry"]
+
+    def as_int_box(box):
+        return tuple(int(round(v)) for v in box)
+
+    tb = as_int_box(geometry["target_box"])
+    ab = as_int_box(geometry["actual_box"])
+    cv2.rectangle(out, (tb[0], tb[1]), (tb[2], tb[3]), (0, 200, 0), 2)
+    cv2.rectangle(out, (ab[0], ab[1]), (ab[2], ab[3]), (30, 80, 230), 2)
+
+    # Tint the actual ink, not the hull -- see GeometryResult's
+    # display_contours docstring: signature_polygon_px is the convex hull
+    # (correct for coverage/IoU math, matches projectA's own computation),
+    # but "bridges" gaps between separate ink blobs with straight lines.
+    # display_contours (real per-blob boundaries) fixes that; falls back
+    # to the hull for older metadata that predates this field.
+    contours = geometry.get("display_contours") or []
+    polys = contours if contours else ([geometry.get("signature_polygon_px")] if geometry.get("signature_polygon_px") else [])
+    if polys:
+        filled = out.copy()
+        for poly in polys:
+            if len(poly) >= 3:
+                pts = np.array(poly, dtype=np.int32).reshape(-1, 1, 2)
+                cv2.fillPoly(filled, [pts], color=(220, 30, 30))
+        out = cv2.addWeighted(filled, 0.3, out, 0.7, 0)
+
+    return out
+
+
+def _crop_region(image: np.ndarray, box: list, pad: int = 25) -> np.ndarray:
+    h, w = image.shape[:2]
+    x1, y1, x2, y2 = (int(round(v)) for v in box)
+    x1, y1 = max(x1 - pad, 0), max(y1 - pad, 0)
+    x2, y2 = min(x2 + pad, w), min(y2 + pad, h)
+    if x2 <= x1 or y2 <= y1:
+        return image
+    return image[y1:y2, x1:x2]
+
+
+def _reconstruct_mask(shape: tuple[int, int], box: list, polygon: list, contours: "list | None" = None) -> np.ndarray:
+    mask = np.zeros(shape, dtype=np.uint8)
+    polys = contours if contours else ([polygon] if len(polygon) >= 3 else [])
+    for poly in polys:
+        if len(poly) >= 3:
+            cv2.fillPoly(mask, [np.array(poly, dtype=np.int32).reshape(-1, 1, 2)], 255)
+    x1, y1, x2, y2 = (int(round(v)) for v in box)
+    pad = 25
+    x1c, y1c = max(x1 - pad, 0), max(y1 - pad, 0)
+    x2c, y2c = min(x2 + pad, shape[1]), min(y2 + pad, shape[0])
+    if x2c <= x1c or y2c <= y1c:
+        return mask
+    return mask[y1c:y2c, x1c:x2c]
+
+
+def save_visualization(path: "str | Path", final_image: np.ndarray, metadata: dict[str, Any]) -> None:
+    """Render and save a multi-panel debug figure for one generated sample.
+
+    Args:
+        path: Output PNG path.
+        final_image: The fully rendered (and augmented) document.
+        metadata: The sample's full metadata dict, as built by
+            :class:`~src.metadata.metadata_exporter.MetadataExporter`.
+    """
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    geometry = metadata["geometry"]
+    difficulty = metadata["difficulty"]
+    metrics = metadata["metrics"]
+
+    overlaid = _draw_overlays(final_image, metadata)
+    crop = _crop_region(final_image, geometry["actual_box"])
+    mask = _reconstruct_mask(final_image.shape[:2], geometry["actual_box"], geometry["signature_polygon_px"], geometry.get("display_contours"))
+
+    fig = plt.figure(figsize=(15, 9.5), dpi=110)
+    fig.suptitle(
+        f"{metadata['document_id']}  |  template={metadata['template']}  |  box={metadata['geometry']['target_box']}",
+        x=0.02, ha="left", fontsize=11, family="monospace",
+    )
+    grid = fig.add_gridspec(2, 3, width_ratios=[2.0, 1.0, 1.0], height_ratios=[1.0, 1.0])
+
+    ax_full = fig.add_subplot(grid[:, 0])
+    ax_full.imshow(overlaid)
+    ax_full.axis("off")
+
+    ax_crop = fig.add_subplot(grid[0, 1])
+    ax_crop.imshow(crop)
+    ax_crop.set_title("Signature field (cropped)")
+    ax_crop.axis("off")
+
+    ax_mask = fig.add_subplot(grid[0, 2])
+    ax_mask.imshow(mask, cmap="gray")
+    ax_mask.set_title("Detected ink mask")
+    ax_mask.axis("off")
+
+    ax_metrics = fig.add_subplot(grid[1, 1])
+    ax_metrics.axis("off")
+    metric_lines = [
+        f"box_coverage_pct:  {metrics['box_coverage_pct']:.2f}",
+        f"containment_pct:   {metrics['containment_pct']:.2f}",
+        f"ink_fill_ratio:    {metrics['ink_fill_ratio']:.3f}",
+        f"polygon_overlap:   {metrics['polygon_overlap_proxy']:.3f}",
+        f"dist_from_center:  {metrics['distance_from_box_center_px']:.1f}px",
+        f"margin_util:       {metrics['margin_utilization']:.3f}",
+        f"mean_dark_int:     {metrics['mean_dark_intensity']:.1f}",
+        f"gray_stddev:       {metrics['gray_stddev']:.1f}",
+        f"signer:            {metadata['signer']['name']} ({metadata['signer']['role']})",
+    ]
+    ax_metrics.text(0.0, 1.0, "\n".join(metric_lines), va="top", ha="left", family="monospace", fontsize=9)
+    ax_metrics.set_title("Coverage metrics")
+
+    ax_diff = fig.add_subplot(grid[1, 2])
+    components = ["misplacement", "whitespace", "faintness", "smallness", "low_texture"]
+    values = [difficulty[c] for c in components]
+    colors = ["#d62728" if v >= 0.66 else "#ff7f0e" if v >= 0.33 else "#2ca02c" for v in values]
+    ax_diff.barh(components, values, color=colors)
+    ax_diff.set_xlim(0, 1)
+    ax_diff.set_title(f"Difficulty: {difficulty['score']:.3f} ({difficulty['tier']})")
+
+    fig.tight_layout(rect=(0.0, 0.0, 1.0, 0.94))
+    fig.savefig(path)
+    plt.close(fig)
diff --git a/unified_pipeline/streamlit_app.py b/unified_pipeline/streamlit_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d79ddc53e73671560576bc68687dfc1882a88b85
--- /dev/null
+++ b/unified_pipeline/streamlit_app.py
@@ -0,0 +1,500 @@
+"""Interactive UI for customizable synthetic signed-document creation.
+
+A thin Streamlit front-end over the existing pipeline (``src/generator.py``,
+``src/config/loader.py``) -- every knob here maps directly to a config
+override the CLI (``main.py --set key=value``) already accepts, so a
+document generated here is reproducible from the command line too (the
+exact overrides used are shown in an expander after each generation).
+
+Run with:
+    streamlit run streamlit_app.py
+"""
+
+from __future__ import annotations
+
+import sys
+import tempfile
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(ROOT))
+
+import numpy as np
+import streamlit as st
+
+from src.analysis.difficulty_analyzer import DifficultyAnalyzer
+from src.analysis.geometry_analyzer import GeometryAnalyzer
+from src.augmentation.augmentation_engine import AugmentationEngine
+from src.config.loader import load_config
+from src.config.schema import DifficultyWeights, PipelineConfig
+from src.generator import GeneratorContext, _export_reference_signature, compute_extra_signature_overlays
+from src.templates.template_provider import template_kind
+from src.utils.image_io import load_image_rgb, save_image_rgb
+from src.utils.random_utils import SeedManager
+from src.verification.case_types import CASE_TABLE
+from src.visualization.before_after_preview import save_before_after_preview
+
+st.set_page_config(page_title="Signature Document Generator", page_icon="\U0001F58A️", layout="wide")
+
+# (label, metadata["difficulty"] key, one-line explanation) -- display order
+# and weights match configs/default.yaml's difficulty: section (and the
+# same breakdown shown in before_after_preview.py's info panel).
+_DIFFICULTY_COMPONENTS = [
+    ("Misplacement", "misplacement", "How far the signature sits from where it should be"),
+    ("Whitespace", "whitespace", "How much of the signature box is empty, unused space"),
+    ("Faintness", "faintness", "How light/faint the ink is"),
+    ("Smallness", "smallness", "How small the signature is relative to the page"),
+    ("Low texture", "low_texture", "How little stroke-to-stroke variation the ink has"),
+]
+
+# Every document-level (post-signing, whole-page) effect
+# AugmentationEngine.apply_to_document() knows about.
+_DOCUMENT_DEGRADATION_EFFECTS = [
+    "perspective", "contrast", "gamma", "coffee_stain", "stain", "shadow",
+    "crumple", "discoloration", "page_tilt", "dust", "scanner_streaks",
+    "scanner_noise", "salt_pepper", "jpeg_compression",
+]
+
+# Signature-level effects (AugmentationEngine.apply_to_signature()) --
+# applied to the signature snippet BEFORE it's composited onto the page.
+# ctx.last_clean_image is captured after this compositing, so any of these
+# that fired are already baked into "Clean" as plain pixels -- the lever
+# below (which only replays apply_to_document() on that already-composited
+# image) structurally cannot show, hide, or scale them.
+_SIGNATURE_LEVEL_EFFECTS = [
+    "rotation", "stroke_dilation", "motion_blur", "gaussian_blur", "ink_fading", "ink_bleed",
+]
+
+
+def _real_effect_strengths(metadata: dict) -> dict[str, float]:
+    """The strength each document-level effect was ACTUALLY given for this
+    sample (0.0 for anything that didn't fire) -- the lever's "match this
+    document" mode scales these rather than forcing every effect on, so at
+    100% it reproduces the real degradation instead of an unrealistic
+    all-14-effects-at-once worst case no real generation ever produces."""
+    effects = (metadata.get("augmentation") or {}).get("effects") or {}
+    return {
+        name: float(effects[name]["strength"])
+        for name in _DOCUMENT_DEGRADATION_EFFECTS
+        if effects.get(name, {}).get("applied") and effects[name].get("strength") is not None
+    }
+
+
+@st.cache_data(show_spinner=False)
+def _degrade_preview(
+    clean_image: "np.ndarray", seed: int, strengths: "dict[str, float]", baseline_image: "np.ndarray | None" = None,
+) -> "tuple[np.ndarray, np.ndarray | None]":
+    """Re-applies document-level degradation to an already-rendered CLEAN
+    image at manually-controlled per-effect strengths -- cheap (no dataset
+    scan, no template render, no signature placement), so moving a slider
+    feels live instead of re-running the whole pipeline per tick. Only
+    effects present in ``strengths`` with a strength > 0 are enabled; every
+    other effect stays off (no all-14-at-once forcing). The same `seed` is
+    reused across every slider position (only the strengths vary) so e.g.
+    the stain's shape/the shadow's corner stays the same as you drag --
+    only intensity changes, not which random variant got picked.
+
+    ``baseline_image``, if given, gets the same geometric transform(s) as
+    ``clean_image`` (perspective/page_tilt/crumple, if any of those are in
+    ``strengths``) applied via ``AugmentationEngine.apply_to_document``'s
+    own ``baseline_image`` passthrough -- returned as the 2nd tuple element,
+    still pixel-aligned with the degraded preview, for
+    :func:`_lever_difficulty` to use.
+    """
+    base_cfg = load_config().augmentation
+    aug_cfg = base_cfg.model_copy(deep=True)
+    for name in _DOCUMENT_DEGRADATION_EFFECTS:
+        effect = getattr(aug_cfg, name)
+        strength = strengths.get(name, 0.0)
+        effect.enabled = strength > 0
+        effect.probability = 1.0 if strength > 0 else 0.0
+        effect.strength = strength
+    engine = AugmentationEngine(aug_cfg)
+    sm = SeedManager(seed, "ui_degradation_lever")
+    degraded, _records, warped_baseline = engine.apply_to_document(clean_image, sm, baseline_image=baseline_image)
+    return degraded, warped_baseline
+
+
+def _lever_difficulty(
+    image: "np.ndarray", target_box: tuple, canvas_size: tuple,
+    baseline_image: "np.ndarray | None", weights: DifficultyWeights,
+):
+    """Re-detects the signature in a lever preview image and recomputes its
+    difficulty score -- so dragging the lever shows not just what the
+    document would look like, but what it would SCORE. ``baseline_image``
+    should already be geometrically aligned with ``image`` (see
+    :func:`_degrade_preview`'s 2nd return value) -- no need to guess
+    whether a geometric effect fired anymore, alignment is guaranteed by
+    construction. Not cached (single small-image geometry pass per rerun --
+    cheap enough to feel live, and ``DifficultyWeights`` isn't reliably
+    hashable for ``st.cache_data`` anyway)."""
+    geometry = GeometryAnalyzer().analyze(image, target_box=target_box, canvas_size=canvas_size, baseline_image_rgb=baseline_image)
+    return DifficultyAnalyzer(weights).analyze(geometry)
+
+
+@st.cache_resource(show_spinner="Discovering templates...")
+def _provider_context(provider: str) -> GeneratorContext:
+    """A `GeneratorContext` built with every OTHER knob left at default --
+    template ids and blank-template preview images depend only on `provider`
+    (scanned/procedural/both), never on case type/difficulty/seed/etc., so
+    this is cached per provider and reused for both listing ids and loading
+    a preview image, instead of rebuilding (and re-rendering all 15
+    procedural templates) on every rerun."""
+    cfg = load_config(overrides=[f"paths.template_provider={provider}"])
+    return GeneratorContext(cfg)
+
+
+def _discover_template_ids(provider: str) -> list[str]:
+    return sorted(_provider_context(provider).template_provider.list_ids())
+
+
+@st.cache_data(show_spinner=False)
+def _template_preview(provider: str, template_id: str) -> "tuple[np.ndarray, str]":
+    """The blank template image (before any signature/cosmetic text is
+    drawn) plus a one-line caption -- cached per (provider, template_id) so
+    flipping through the dropdown doesn't re-decode the same image or
+    re-derive the same caption on every keystroke/rerun."""
+    template = _provider_context(provider).template_provider.get(template_id)
+    image = load_image_rgb(template.image_path)
+    page = template.annotation.pages[0]
+    n_boxes = len(page.signature_boxes)
+    roles = ", ".join(b.role for b in page.signature_boxes)
+    caption = (
+        f"{template_id} ({template_kind(template_id)}) -- {template.annotation.document_type} -- "
+        f"{n_boxes} signature box{'es' if n_boxes != 1 else ''}: {roles}"
+    )
+    return image, caption
+
+
+def _build_context(overrides: list[str]) -> tuple[GeneratorContext, PipelineConfig]:
+    config = load_config(overrides=overrides)
+    return GeneratorContext(config), config
+
+
+st.title("\U0001F58A️ Synthetic Signed-Document Generator")
+st.caption(
+    "Every control below is a config override this pipeline's CLI already accepts "
+    "(`main.py --set key=value`) -- generated here or from the command line, the same "
+    "seed + settings always produce the exact same document."
+)
+
+with st.sidebar:
+    st.header("Template")
+    provider = st.selectbox("Template provider", ["both", "scanned", "procedural"], index=0)
+    template_ids = _discover_template_ids(provider)
+    template_choice = st.selectbox("Template", ["Random"] + template_ids)
+
+    if template_choice != "Random":
+        preview_image, preview_caption = _template_preview(provider, template_choice)
+        st.image(preview_image, caption=preview_caption, use_container_width=True)
+    else:
+        st.caption("A template will be picked at random for each generation.")
+
+    st.header("Case type")
+    case_labels = {"(generic sample -- no case type)": None}
+    for code, spec in CASE_TABLE.items():
+        case_labels[f"{code} -- {spec.description} ({spec.verdict})"] = code
+    case_label = st.selectbox("Case type", list(case_labels.keys()))
+    case_type = case_labels[case_label]
+
+    st.header("Difficulty")
+    target_enabled = st.checkbox("Target a specific difficulty", value=False)
+    target_difficulty = None
+    tolerance = 0.05
+    if target_enabled:
+        target_pct = st.slider("Target difficulty", 0, 100, 50, 1, format="%d%%")
+        tolerance_pct = st.slider("Tolerance", 1, 30, 5, 1, format="±%d%%")
+        target_difficulty = target_pct / 100.0
+        tolerance = tolerance_pct / 100.0
+
+    force_deck_literal_difficulty = st.checkbox(
+        "Force deck-literal reference for ALL templates",
+        value=False,
+        help=(
+            "By default (unchecked) each sample is auto-calibrated by its own template kind: "
+            "procedural samples use smallness_reference_area_ratio=0.003 / faintness range 165-220 / "
+            "low_texture_reference_stddev=46 (refit to this pipeline's own measured proportions -- "
+            "real signature area is only ~0.2-1.5% of the page vs. the deck's assumed 4% 'normal'), "
+            "scanned samples use the deck-literal 0.04 / 90-210 / 60. This happens automatically per "
+            "sample now, including within a 'both'-provider batch -- no need to opt in. Check this box "
+            "only to force the OLD deck-literal constants onto every sample regardless of template "
+            "kind, e.g. to compare against pre-recalibration scores."
+        ),
+    )
+
+    st.header("Signer")
+    signer_name = st.text_input("Name override (optional)")
+    signer_role = st.text_input("Role override (optional)")
+
+    st.header("Cosmetic fields")
+    enable_cosmetic_fields = st.checkbox("Fill name/company/SWIFT/amount fields", value=True)
+
+    st.header("Reproducibility")
+    seed = st.number_input("Seed", min_value=0, value=42, step=1)
+
+    st.header("Preview extras")
+    show_before_after = st.checkbox("Show before/after degradation preview", value=True)
+
+    generate = st.button("Generate document", type="primary", use_container_width=True)
+
+if generate:
+    overrides = [
+        f"seed={int(seed)}",
+        f"paths.template_provider={provider}",
+        f"document.enable_cosmetic_fields={'true' if enable_cosmetic_fields else 'false'}",
+    ]
+    if force_deck_literal_difficulty:
+        overrides += [
+            "difficulty.smallness_reference_area_ratio=0.04",
+            "difficulty.faintness_dark_ref=90",
+            "difficulty.faintness_light_ref=210",
+            "difficulty.low_texture_reference_stddev=60",
+        ]
+    if template_choice != "Random":
+        overrides.append(f"document.template={template_choice}")
+    if case_type:
+        overrides.append(f"quality.case_type={case_type}")
+    if signer_name.strip():
+        overrides.append(f"signer.name={signer_name.strip()}")
+    if signer_role.strip():
+        overrides.append(f"signer.role={signer_role.strip()}")
+
+    with st.spinner("Generating document..."):
+        ctx, config = _build_context(overrides)
+        if target_difficulty is not None:
+            metadata, image, report = ctx.run_sample_targeting_difficulty(1, target_difficulty, tolerance)
+        else:
+            metadata, image, report = ctx.run_sample(1)
+
+    st.session_state["result"] = {
+        "metadata": metadata, "image": image, "report": report,
+        "config": config, "ctx": ctx, "overrides": overrides,
+        "target_difficulty": target_difficulty, "tolerance": tolerance,
+    }
+
+if "result" in st.session_state:
+    r = st.session_state["result"]
+    metadata, image, report, config, ctx = r["metadata"], r["image"], r["report"], r["config"], r["ctx"]
+
+    col_img, col_info = st.columns([2, 1])
+    with col_img:
+        st.image(image, caption=f"{metadata['template']} ({metadata['template_kind']}) -- {metadata['document_type']}", use_container_width=True)
+    with col_info:
+        v = metadata.get("verification") or {}
+        verdict = v.get("verdict")
+        if verdict == "PASS":
+            st.success(f"Verdict: {verdict}")
+        elif verdict == "REJECT":
+            st.error(f"Verdict: {verdict} -- {v.get('reject_code')}")
+        else:
+            st.info("Generic sample (no case-type verdict)")
+        st.metric("Difficulty", f"{metadata['difficulty']['score'] * 100:.0f}%")
+        st.caption(
+            f"tier: {metadata['difficulty']['tier']} (kept in the ground-truth data for case-type "
+            "validation -- see the Difficulty & degradation tab for what actually built this number)"
+        )
+        st.write(f"**Signer:** {metadata['signer']['name']} ({metadata['signer']['role']})")
+        if report is not None:
+            st.write(f"**Validation:** {'✅ passed' if report.passed else '❌ failed -- ' + '; '.join(i.message for i in report.errors)}")
+        targeting = metadata.get("difficulty_targeting")
+        if targeting:
+            match_str = "matched" if targeting["matched"] else "closest attempt (no match within tolerance)"
+            st.caption(f"Difficulty targeting: {match_str}, {targeting['attempts_used']} attempt(s)")
+
+    tab_difficulty, tab_lever, tab_before_after, tab_export = st.tabs(
+        ["Difficulty & degradation", "Interactive lever", "Before / After (real)", "Ground truth & export"]
+    )
+
+    with tab_difficulty:
+        bd_col, deg_col = st.columns(2)
+        with bd_col:
+            st.subheader("What built the difficulty score")
+            difficulty = metadata["difficulty"]
+            weights = config.difficulty
+            for label, key, explanation in _DIFFICULTY_COMPONENTS:
+                value = difficulty[key]
+                weight = getattr(weights, key)
+                st.progress(value, text=f"**{label}** -- {value * 100:.0f}% (weight {weight * 100:.0f}% of score)")
+                st.caption(explanation)
+        with deg_col:
+            st.subheader("Degradation applied")
+            st.caption("What actually, randomly fired for THIS document -- see the Interactive lever tab to explore it live.")
+            effects = (metadata.get("augmentation") or {}).get("effects") or {}
+            applied = sorted(name for name, rec in effects.items() if rec.get("applied"))
+            not_applied = sorted(name for name, rec in effects.items() if not rec.get("applied"))
+            if applied:
+                for name in applied:
+                    strength = effects[name].get("strength")
+                    st.write(f"✅ **{name}** -- strength {strength}")
+            else:
+                st.write("No document/signature-layer degradation effects fired for this sample.")
+            if not_applied:
+                with st.expander(f"{len(not_applied)} effect(s) not applied"):
+                    st.write(", ".join(not_applied))
+
+    with tab_lever:
+        if ctx.last_clean_image is None:
+            st.info("No clean render available for this sample.")
+        else:
+            real_strengths = _real_effect_strengths(metadata)
+            effects = (metadata.get("augmentation") or {}).get("effects") or {}
+            sig_level_fired = [
+                (name, effects[name].get("strength"))
+                for name in _SIGNATURE_LEVEL_EFFECTS
+                if effects.get(name, {}).get("applied")
+            ]
+            st.caption(
+                "This lever only controls whole-page effects (perspective/contrast/stains/shadow/tilt/"
+                "scanner noise/JPEG/etc.). Signature-only effects -- rotation, stroke thickness, ink "
+                "bleed/fading, blur -- are applied earlier, to the signature alone, before it's pasted onto "
+                "the page, so they're already permanently baked into \"Clean\" below and can't be toggled here."
+            )
+            if sig_level_fired:
+                fired_str = ", ".join(f"{name} (strength={strength})" for name, strength in sig_level_fired)
+                st.warning(
+                    f"For THIS sample, {len(sig_level_fired)} signature-level effect(s) actually fired and are "
+                    f"already part of \"Clean\": {fired_str}. The lever's 100% will look less degraded than the "
+                    f"real AFTER image (Before/After tab) by exactly that much."
+                )
+            lever_enabled = st.checkbox("Enable interactive degradation lever", value=False)
+            if lever_enabled:
+                mode = st.radio(
+                    "Mode",
+                    ["Match this document (scaled)", "Adjust each effect individually", "Explore from clean (all effects)"],
+                    horizontal=True,
+                )
+                if mode == "Match this document (scaled)":
+                    st.caption(
+                        "Scales ONLY the effects that actually fired on this document, together, from 0% (clean) "
+                        "to 100% (exactly this document's real strengths -- same effects, same seed, so it "
+                        "should closely match the 'Before / After (real)' tab, modulo any random-variant "
+                        "differences from the lever using its own seed key)."
+                    )
+                    if not real_strengths:
+                        st.write("No document-level effects fired for this sample -- nothing to scale.")
+                        strengths = {}
+                    else:
+                        lever_pct = st.slider("Degradation strength", 0, 100, 100, 1, format="%d%%", key="degradation_lever_scaled")
+                        frac = lever_pct / 100.0
+                        strengths = {name: value * frac for name, value in real_strengths.items()}
+                elif mode == "Adjust each effect individually":
+                    st.caption(
+                        "One slider per document-level effect, each starting from what actually fired on this "
+                        "document (0% for anything that didn't). Move any of them independently to explore "
+                        "'what if this effect were stronger/weaker/on'."
+                    )
+                    strengths = {}
+                    with st.expander("Per-effect strength", expanded=True):
+                        for name in _DOCUMENT_DEGRADATION_EFFECTS:
+                            default_pct = int(round(real_strengths.get(name, 0.0) * 100))
+                            pct = st.slider(name, 0, 100, default_pct, 1, format="%d%%", key=f"degradation_lever_{name}")
+                            strengths[name] = pct / 100.0
+                else:
+                    st.caption(
+                        "Ignores what actually fired on this document -- every one of the 14 whole-page effects "
+                        "is forced ON together, starting from clean, at a single shared strength. A generic "
+                        "'how bad could it get' worst-case explorer, not specific to this sample (this is what "
+                        "the very first version of this lever always did; kept as its own mode since it's still "
+                        "useful for stress-testing, just no longer the default)."
+                    )
+                    st.caption(
+                        "⚠️ Known limitation: forcing scanner_noise/salt_pepper/dust on simultaneously "
+                        "with everything else can make the ink detector itself unreliable at higher strengths here "
+                        "-- scattered noise specks can inflate the detected signature's bounding box even though "
+                        "the noise has nothing to do with the real signature, making the score read easier, not "
+                        "harder, the more you push this slider. Real generation never stacks effects this way "
+                        "(each fires independently/probabilistically), so this is specific to this stress-test "
+                        "mode, not a sign real dataset scores are wrong the way the page_tilt bug was."
+                    )
+                    lever_pct = st.slider("Degradation strength", 0, 100, 0, 1, format="%d%%", key="degradation_lever_from_clean")
+                    if lever_pct >= 25:
+                        st.warning(
+                            f"At {lever_pct}%, the difficulty_score below is likely unreliable (see the "
+                            "limitation noted above) -- treat the IMAGE as the honest signal here, not the score."
+                        )
+                    frac = lever_pct / 100.0
+                    strengths = {name: frac for name in _DOCUMENT_DEGRADATION_EFFECTS}
+
+                degraded_preview, degraded_baseline = _degrade_preview(
+                    ctx.last_clean_image, int(seed), strengths, baseline_image=ctx.last_baseline_image,
+                )
+                lever_col1, lever_col2 = st.columns(2)
+                with lever_col1:
+                    st.image(ctx.last_clean_image, caption="Clean", use_container_width=True)
+                    if ctx.last_target_box is not None and ctx.last_canvas_size is not None:
+                        clean_score = _lever_difficulty(
+                            ctx.last_clean_image, ctx.last_target_box, ctx.last_canvas_size,
+                            ctx.last_baseline_image, config.difficulty,
+                        )
+                        st.caption(f"difficulty_score: {clean_score.score * 100:.0f}% ({clean_score.tier})")
+                with lever_col2:
+                    st.image(degraded_preview, caption="Degraded (lever)", use_container_width=True)
+                    if ctx.last_target_box is not None and ctx.last_canvas_size is not None:
+                        degraded_score = _lever_difficulty(
+                            degraded_preview, ctx.last_target_box, ctx.last_canvas_size,
+                            degraded_baseline, config.difficulty,
+                        )
+                        st.caption(f"difficulty_score: {degraded_score.score * 100:.0f}% ({degraded_score.tier})")
+                    else:
+                        st.caption("difficulty_score unavailable (regenerate this sample to compute it live).")
+                if ctx.last_target_box is not None and ctx.last_canvas_size is not None:
+                    st.caption(
+                        "Each score above is freshly re-detected from the pictured image (same "
+                        "GeometryAnalyzer/DifficultyAnalyzer the real pipeline uses, with the blank-template "
+                        "baseline re-warped through the same perspective/page_tilt/crumple transform so ink "
+                        "detection's printed-content exclusion still works correctly) -- it can still differ "
+                        "slightly from the 'Difficulty' badge at the top of the page, which reflects the actual "
+                        "generated document under its own real random effect draws, not this lever's."
+                    )
+
+    with tab_before_after:
+        if show_before_after and ctx.last_clean_image is not None:
+            extra_boxes = compute_extra_signature_overlays(ctx, metadata, image)
+            tmp_path = Path(tempfile.gettempdir()) / "unified_pipeline_ui_before_after.png"
+            save_before_after_preview(tmp_path, ctx.last_clean_image, image, metadata, config.difficulty, extra_boxes)
+            st.image(str(tmp_path), caption="Before / after degradation preview", use_container_width=True)
+            st.caption(
+                "BEFORE is a measurement diagnostic: the colored boxes/fill mark the target box, detected "
+                "signature, and cosmetic fields used for scoring. AFTER intentionally has no overlay -- it "
+                "shows the final document as a real reviewer/scanner would see it, with whatever effects "
+                "actually, randomly fired (see the info panel under BEFORE for the exact list)."
+            )
+        elif not show_before_after:
+            st.info("Enable \"Show before/after degradation preview\" in the sidebar to see this.")
+        else:
+            st.info("No clean render available for this sample.")
+
+    with tab_export:
+        st.subheader("Ground truth JSON")
+        st.json(metadata)
+
+        st.subheader("Equivalent CLI command")
+        st.caption("For reproducing this exact document from the command line.")
+        cli_parts = ["python main.py --count 1"]
+        if r["target_difficulty"] is not None:
+            cli_parts.append(f"--target-difficulty {r['target_difficulty']:.2f} --difficulty-tolerance {r['tolerance']:.2f}")
+        cli_parts += [f"--set {o}" for o in r["overrides"]]
+        st.code(" ".join(cli_parts), language="bash")
+
+        st.subheader("Save this case")
+        save_col1, save_col2 = st.columns([3, 1])
+        with save_col1:
+            case_name = st.text_input("Case folder name", value=metadata["document_id"], label_visibility="collapsed")
+        with save_col2:
+            if st.button("Save to data/ui_generated/", use_container_width=True):
+                out_dir = ROOT / "data" / "ui_generated" / case_name
+                out_dir.mkdir(parents=True, exist_ok=True)
+                save_image_rgb(out_dir / f"{case_name}_document.png", image)
+                ctx.metadata_exporter.export(metadata, out_dir / f"{case_name}_ground_truth.json")
+                sig_source = metadata.get("signature_source") or {}
+                if sig_source.get("path"):
+                    try:
+                        _export_reference_signature(sig_source["path"], out_dir / f"{case_name}_reference_signature.png")
+                    except OSError:
+                        pass
+                st.success(f"Saved to {out_dir}")
+else:
+    st.info("Set your options in the sidebar and click **Generate document** to get started.")
+    if template_choice != "Random":
+        preview_image, preview_caption = _template_preview(provider, template_choice)
+        st.image(preview_image, caption=f"Blank template preview -- {preview_caption}", width=500)
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+++ b/unified_pipeline/templates/scanned/mandate/bank_doc_09.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ddd972ab7a0d55c8f84bf1f5ed39b8c039aba64979c79ef6727cc56c2900d450
+size 1128098
diff --git a/unified_pipeline/tests/__init__.py b/unified_pipeline/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/unified_pipeline/tests/conftest.py b/unified_pipeline/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..70d5b48b26c5ce1de7dda99d36dc1d1986842da8
--- /dev/null
+++ b/unified_pipeline/tests/conftest.py
@@ -0,0 +1,56 @@
+"""Shared pytest fixtures pointing at the project's real bundled assets."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+
+
+@pytest.fixture(scope="session")
+def project_root() -> Path:
+    return PROJECT_ROOT
+
+
+@pytest.fixture(scope="session")
+def image_dir(project_root: Path) -> Path:
+    return project_root / "templates" / "scanned"
+
+
+@pytest.fixture(scope="session")
+def dataset_dir(project_root: Path) -> Path:
+    return project_root / "data" / "raw" / "cedar"
+
+
+@pytest.fixture(scope="session")
+def annotations_dir(project_root: Path) -> Path:
+    return project_root / "annotations"
+
+
+# The additive signature sources merged in by the unified-pipeline merge --
+# see src/signatures/signature_dataset.py. Copied in under data/raw/ so
+# this repo is self-contained (no sibling checkouts required).
+
+@pytest.fixture(scope="session")
+def signet_dir(project_root: Path) -> Path:
+    return project_root / "data" / "raw" / "signet"
+
+
+@pytest.fixture(scope="session")
+def gpds_dir(project_root: Path) -> Path:
+    # Not bundled (see README) -- returns a path that legitimately doesn't
+    # exist, so SignatureDataset(gpds_dir=...) exercises the "source not
+    # present, skip gracefully" path rather than the discovery path.
+    return project_root / "data" / "raw" / "gpds"
+
+
+@pytest.fixture(scope="session")
+def bhsig260_hindi_dir(project_root: Path) -> Path:
+    return project_root / "data" / "raw" / "bhsig260_hindi"
+
+
+@pytest.fixture(scope="session")
+def bhsig260_bengali_dir(project_root: Path) -> Path:
+    return project_root / "data" / "raw" / "bhsig260_bengali"
diff --git a/unified_pipeline/tests/test_difficulty_analyzer.py b/unified_pipeline/tests/test_difficulty_analyzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..508873f34d3e5148e24e02b66a1b469a3dd457a8
--- /dev/null
+++ b/unified_pipeline/tests/test_difficulty_analyzer.py
@@ -0,0 +1,143 @@
+"""Unit tests for src.analysis.difficulty_analyzer -- verifies the exact spec formula."""
+
+from __future__ import annotations
+
+import math
+
+from src.analysis.difficulty_analyzer import DifficultyAnalyzer
+from src.analysis.geometry_analyzer import GeometryResult
+from src.config.schema import DifficultyWeights
+
+
+def _geometry(
+    polygon_overlap_proxy: float, whitespace_ratio: float, mean_dark_intensity: float,
+    signature_area_ratio: float, gray_stddev: float,
+) -> GeometryResult:
+    box = (0.0, 0.0, 100.0, 20.0)
+    return GeometryResult(
+        found=True, signature_bbox_px=box, min_rotated_rect={}, signature_polygon_px=[[0, 0], [1, 0], [1, 1]],
+        convex_hull_px=[], display_contours=[], centroid_px=(50.0, 10.0), orientation_deg=0.0, ink_pixels=100,
+        target_box=box, actual_box=box, search_box=box,
+        box_coverage_pct=0.0, containment_pct=0.0, ink_fill_ratio=1 - whitespace_ratio,
+        polygon_overlap_proxy=polygon_overlap_proxy, distance_from_box_center_px=0.0, margin_utilization=0.0,
+        mean_dark_intensity=mean_dark_intensity, gray_stddev=gray_stddev,
+        signature_area_ratio=signature_area_ratio, whitespace_ratio=whitespace_ratio,
+    )
+
+
+def test_difficulty_score_matches_exact_spec_formula():
+    weights = DifficultyWeights()
+    analyzer = DifficultyAnalyzer(weights)
+
+    geom = _geometry(
+        polygon_overlap_proxy=0.4,  # misplacement = 0.6
+        whitespace_ratio=0.5,       # whitespace = 0.5
+        mean_dark_intensity=150.0,  # faintness = clamp((150-90)/120) = 0.5
+        signature_area_ratio=0.02,  # smallness = clamp(1 - 0.02/0.04) = 0.5
+        gray_stddev=30.0,           # low_texture = clamp(1 - 30/60) = 0.5
+    )
+    result = analyzer.analyze(geom)
+
+    assert math.isclose(result.misplacement, 0.6, abs_tol=1e-6)
+    assert math.isclose(result.whitespace, 0.5, abs_tol=1e-6)
+    assert math.isclose(result.faintness, 0.5, abs_tol=1e-6)
+    assert math.isclose(result.smallness, 0.5, abs_tol=1e-6)
+    assert math.isclose(result.low_texture, 0.5, abs_tol=1e-6)
+
+    expected = 0.35 * 0.6 + 0.20 * 0.5 + 0.20 * 0.5 + 0.15 * 0.5 + 0.10 * 0.5
+    assert math.isclose(result.score, expected, abs_tol=1e-6)
+
+
+def test_difficulty_components_are_clamped_to_unit_interval():
+    weights = DifficultyWeights()
+    analyzer = DifficultyAnalyzer(weights)
+
+    geom = _geometry(
+        polygon_overlap_proxy=-5.0,     # misplacement would exceed 1 -> clamp to 1
+        whitespace_ratio=2.0,           # clamp to 1
+        mean_dark_intensity=1000.0,     # faintness clamp to 1
+        signature_area_ratio=-1.0,      # smallness clamp to 1
+        gray_stddev=-10.0,              # low_texture clamp to 1
+    )
+    result = analyzer.analyze(geom)
+
+    assert result.misplacement == 1.0
+    assert result.whitespace == 1.0
+    assert result.faintness == 1.0
+    assert result.smallness == 1.0
+    assert result.low_texture == 1.0
+    assert result.score == 1.0
+    assert result.tier == "hard"
+
+
+def test_difficulty_tier_boundaries():
+    weights = DifficultyWeights()
+    analyzer = DifficultyAnalyzer(weights)
+
+    # All-zero components -> score 0 -> easy.
+    geom_easy = _geometry(
+        polygon_overlap_proxy=1.0, whitespace_ratio=0.0, mean_dark_intensity=90.0,
+        signature_area_ratio=0.04, gray_stddev=60.0,
+    )
+    assert analyzer.analyze(geom_easy).tier == "easy"
+    assert analyzer.analyze(geom_easy).score == 0.0
+
+    # All components at 1.0 -> score 1.0 -> hard.
+    geom_hard = _geometry(
+        polygon_overlap_proxy=0.0, whitespace_ratio=1.0, mean_dark_intensity=300.0,
+        signature_area_ratio=0.0, gray_stddev=0.0,
+    )
+    assert analyzer.analyze(geom_hard).tier == "hard"
+
+
+def test_difficulty_score_is_reproducible_given_same_geometry():
+    weights = DifficultyWeights()
+    analyzer = DifficultyAnalyzer(weights)
+    geom = _geometry(0.3, 0.4, 140.0, 0.01, 25.0)
+
+    r1 = analyzer.analyze(geom)
+    r2 = analyzer.analyze(geom)
+
+    assert r1 == r2
+
+
+def test_difficulty_weights_must_sum_to_one():
+    import pytest
+    from pydantic import ValidationError
+
+    with pytest.raises(ValidationError):
+        DifficultyWeights(misplacement=0.9, whitespace=0.2, faintness=0.2, smallness=0.15, low_texture=0.10)
+
+
+def test_calibration_constants_default_to_deck_literal_values():
+    """configs/default.yaml (scanned templates) must keep the spec deck's
+    generic literal constants -- see projectA/handoff/SCHEMA_HANDOFF.md
+    section 2a for why these are NOT the same as the procedural profile."""
+    weights = DifficultyWeights()
+    assert weights.smallness_reference_area_ratio == 0.04
+    assert weights.faintness_dark_ref == 90.0
+    assert weights.faintness_light_ref == 210.0
+    assert weights.low_texture_reference_stddev == 60.0
+
+
+def test_recalibrated_profile_produces_a_different_score_for_the_same_geometry():
+    """configs/procedural_templates.yaml overrides these three constants to
+    match projectA's own empirical recalibration (real ink is much smaller/
+    lighter relative to page area on procedural templates than the deck's
+    generic reference assumes). Same geometry, different profile -> the
+    smallness/faintness components (and therefore the overall score) must
+    differ -- this is the config wiring unified_pipeline/README.md and
+    configs/procedural_templates.yaml describe, not a hardcoded constant
+    anymore (see src/analysis/difficulty_analyzer.py)."""
+    geom = _geometry(
+        polygon_overlap_proxy=0.8, whitespace_ratio=0.9, mean_dark_intensity=195.0,
+        signature_area_ratio=0.006, gray_stddev=15.0,
+    )
+    deck_default = DifficultyAnalyzer(DifficultyWeights()).analyze(geom)
+    procedural = DifficultyAnalyzer(DifficultyWeights(
+        smallness_reference_area_ratio=0.003, faintness_dark_ref=184.0, faintness_light_ref=220.0,
+    )).analyze(geom)
+
+    assert deck_default.smallness != procedural.smallness
+    assert deck_default.faintness != procedural.faintness
+    assert deck_default.score != procedural.score
diff --git a/unified_pipeline/tests/test_difficulty_targeting_and_preview.py b/unified_pipeline/tests/test_difficulty_targeting_and_preview.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f878cffcfe91cf8ced38fe2d8fd530819014536
--- /dev/null
+++ b/unified_pipeline/tests/test_difficulty_targeting_and_preview.py
@@ -0,0 +1,137 @@
+"""Tests for numeric difficulty-score targeting (--target-difficulty) and
+the before/after degradation comparison PNG (--save-before-after)."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext, _generate_and_export, _init_worker
+
+
+def _make_context(tmp_path, **overrides_kv):
+    overrides = [f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1"]
+    for k, v in overrides_kv.items():
+        overrides.append(f"{k}={v}")
+    config = load_config(overrides=overrides)
+    return config, GeneratorContext(config)
+
+
+# --------------------------------------------------------------------- #
+# Numeric difficulty targeting
+# --------------------------------------------------------------------- #
+
+def test_target_difficulty_lands_within_tolerance_when_reachable(tmp_path):
+    # 0.75 (not 0.5): under configs/default.yaml's deck-literal difficulty
+    # reference constants, real signed samples cluster in ~0.7-0.82 for this
+    # seed/template set (see DifficultyWeights' schema-default docstring --
+    # a documented, separate calibration characteristic, not something this
+    # test is meant to validate) -- 0.5 stopped landing after the
+    # geometric-transform baseline-exclusion fix (previously, an
+    # ink-detection contamination bug on page_tilt/perspective/crumple
+    # samples was accidentally dragging some scores down into range).
+    _, ctx = _make_context(tmp_path, seed=13)
+    metadata, _, report = ctx.run_sample_targeting_difficulty(1, target_score=0.75, tolerance=0.08, max_attempts=8)
+
+    targeting = metadata["difficulty_targeting"]
+    assert targeting["requested_score"] == 0.75
+    assert abs(metadata["difficulty"]["score"] - 0.75) <= 0.08
+    assert targeting["matched"] is True
+    assert report is not None and report.passed
+
+
+def test_target_difficulty_records_closest_attempt_when_unreachable(tmp_path):
+    """An impossibly tight tolerance forces the "no match, keep the closest"
+    fallback path -- must still return a valid, internally-consistent
+    sample (not crash, not return a mismatched image/metadata pair)."""
+    _, ctx = _make_context(tmp_path, seed=13)
+    metadata, image, report = ctx.run_sample_targeting_difficulty(
+        1, target_score=0.5, tolerance=0.0, max_attempts=3,
+    )
+    targeting = metadata["difficulty_targeting"]
+    assert targeting["matched"] is False
+    assert targeting["attempts_used"] == 3
+    assert isinstance(image, np.ndarray)
+    # The returned clean image (for before/after) must correspond to the
+    # SAME attempt as the returned metadata/image, not just whichever
+    # run_sample() call happened to run last (see the "best" tuple's
+    # clean_image tracking in run_sample_targeting_difficulty()).
+    assert ctx.last_clean_image is not None
+    assert ctx.last_clean_image.shape == image.shape
+
+
+def test_different_targets_produce_different_scores(tmp_path):
+    _, ctx = _make_context(tmp_path, seed=99)
+    metadata_easy, _, _ = ctx.run_sample_targeting_difficulty(1, target_score=0.1, tolerance=0.15, max_attempts=6)
+    metadata_hard, _, _ = ctx.run_sample_targeting_difficulty(2, target_score=0.9, tolerance=0.15, max_attempts=6)
+    assert metadata_easy["difficulty"]["score"] < metadata_hard["difficulty"]["score"]
+
+
+def test_target_difficulty_via_dataset_generator_worker(tmp_path):
+    """End-to-end through the same worker function main.py's batch path
+    uses, not just calling GeneratorContext directly."""
+    config = load_config(overrides=[
+        f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1", "seed=21",
+        "generation.target_difficulty=0.4", "generation.difficulty_tolerance=0.15",
+        "generation.difficulty_max_attempts=6",
+    ])
+    _init_worker(config)
+    outcome = _generate_and_export(1)
+    assert outcome.success
+    json_files = list(Path(tmp_path).rglob("*_ground_truth.json"))
+    assert len(json_files) == 1
+    import json
+    metadata = json.loads(json_files[0].read_text(encoding="utf-8"))
+    assert "difficulty_targeting" in metadata
+    assert metadata["difficulty_targeting"]["requested_score"] == 0.4
+
+
+# --------------------------------------------------------------------- #
+# Before/after preview
+# --------------------------------------------------------------------- #
+
+def test_before_after_preview_written_when_enabled(tmp_path):
+    config = load_config(overrides=[
+        f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1", "seed=4",
+        "generation.save_before_after_preview=true",
+    ])
+    _init_worker(config)
+    outcome = _generate_and_export(1)
+    assert outcome.success
+
+    previews = list(Path(tmp_path).rglob("*_before_after.png"))
+    assert len(previews) == 1
+    from PIL import Image
+    with Image.open(previews[0]) as im:
+        w, h = im.size
+    # Two document panels side by side (10px gap) -- combined width is
+    # always strictly greater than a single panel's height for every page
+    # proportion this pipeline produces (portrait or landscape).
+    assert w > h
+
+
+def test_before_after_preview_not_written_when_disabled(tmp_path):
+    config = load_config(overrides=[f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1", "seed=4"])
+    _init_worker(config)
+    outcome = _generate_and_export(1)
+    assert outcome.success
+    previews = list(Path(tmp_path).rglob("*_before_after.png"))
+    assert len(previews) == 0
+
+
+def test_before_after_preview_before_panel_differs_from_after_when_augmented(tmp_path):
+    """Force at least one document-level effect on so the two panels are
+    actually visibly different (sanity check this isn't just saving the
+    same image twice)."""
+    config = load_config(overrides=[
+        f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1", "seed=4",
+        "generation.save_before_after_preview=true",
+        "augmentation.shadow.enabled=true", "augmentation.shadow.probability=1.0", "augmentation.shadow.strength=0.8",
+    ])
+    ctx = GeneratorContext(config)
+    metadata, final_image, _ = ctx.run_sample(1)
+    assert ctx.last_clean_image is not None
+    assert ctx.last_clean_image.shape == final_image.shape
+    assert not np.array_equal(ctx.last_clean_image, final_image)
diff --git a/unified_pipeline/tests/test_discovery.py b/unified_pipeline/tests/test_discovery.py
new file mode 100644
index 0000000000000000000000000000000000000000..67836edfc2d4b97c2a55ea4e0e177a9e383eb9b9
--- /dev/null
+++ b/unified_pipeline/tests/test_discovery.py
@@ -0,0 +1,149 @@
+"""Integration tests for template and signature dataset discovery against the real bundled assets."""
+
+from __future__ import annotations
+
+from src.signatures.signature_dataset import SignatureDataset
+from src.templates.template_loader import TemplateLoader
+import numpy as np
+
+
+def test_template_loader_discovers_all_nine_templates(image_dir, annotations_dir):
+    loader = TemplateLoader(image_dir, annotations_dir)
+    templates = loader.discover()
+    assert len(templates) == 9
+    assert set(templates.keys()) == {f"bank_doc_{i:02d}" for i in range(1, 10)}
+
+
+def test_every_template_has_at_least_one_valid_signature_box(image_dir, annotations_dir):
+    loader = TemplateLoader(image_dir, annotations_dir)
+    for template_id in loader.list_ids():
+        template = loader.get(template_id)
+        page = template.annotation.pages[0]
+        assert len(page.signature_boxes) >= 1
+        for box in page.signature_boxes:
+            assert box.x2 > box.x1
+            assert box.y2 > box.y1
+            # Box must lie within the annotated page bounds.
+            assert 0 <= box.x1 and box.x2 <= page.page_size[0]
+            assert 0 <= box.y1 and box.y2 <= page.page_size[1]
+
+
+def test_template_loader_random_template_is_deterministic(image_dir, annotations_dir):
+    loader = TemplateLoader(image_dir, annotations_dir)
+    t1 = loader.random_template(np.random.default_rng(123))
+    t2 = loader.random_template(np.random.default_rng(123))
+    assert t1.template_id == t2.template_id
+
+
+def test_signature_dataset_discovers_bundled_dataset(dataset_dir):
+    ds = SignatureDataset(dataset_dir)
+    records = ds.discover()
+    assert len(records) > 0
+    assert any(r.source_type == "genuine" for r in records)
+    assert any(r.source_type == "forged" for r in records)
+
+
+def test_signature_dataset_source_filter(dataset_dir):
+    ds = SignatureDataset(dataset_dir)
+    rng = np.random.default_rng(0)
+    for _ in range(20):
+        record = ds.sample(rng, source_filter="genuine")
+        assert record.source_type == "genuine"
+
+
+def test_signature_dataset_sample_is_deterministic(dataset_dir):
+    ds = SignatureDataset(dataset_dir)
+    r1 = ds.sample(np.random.default_rng(55), source_filter="any")
+    r2 = ds.sample(np.random.default_rng(55), source_filter="any")
+    assert r1.signature_id == r2.signature_id
+
+
+def test_metadata_csv_overrides_signer_identity_and_script(tmp_path):
+    """SignatureDataset's metadata.csv override path (a name/script/source_type
+    per file, taking precedence over filename-pattern inference) is still
+    supported code -- see src/signatures/signature_dataset.py::_load_metadata_csv().
+    Not exercised by bundled fixture data anymore (the unified-pipeline merge
+    dropped Venket's 11-file named-signer demo overlay as redundant with the
+    much larger bundled BHSig260 data -- see unified_pipeline/README.md), so
+    this test builds its own tiny synthetic dataset instead."""
+    (tmp_path / "named").mkdir()
+    (tmp_path / "named" / "sample_01.png").write_bytes(b"\x89PNG\r\n\x1a\n")
+    (tmp_path / "metadata.csv").write_text(
+        "path,signer,source_type,script\nnamed/sample_01.png,Priya Sharma,genuine,hindi\n", encoding="utf-8",
+    )
+    ds = SignatureDataset(tmp_path)
+    records = ds.discover()
+    assert len(records) == 1
+    assert records[0].signer_group == "Priya Sharma"
+    assert records[0].script == "hindi"
+    assert records[0].source_type == "genuine"
+    assert ds.script_for_group("Priya Sharma") == "hindi"
+
+
+def test_default_script_for_unlabeled_records_is_latin(dataset_dir):
+    ds = SignatureDataset(dataset_dir)
+    assert all(r.script == "latin" for r in ds.discover())
+
+
+# --------------------------------------------------------------------- #
+# Additive sources (SigNet / BHSig260 / GPDS) -- unified-pipeline merge.
+# --------------------------------------------------------------------- #
+
+def test_signature_dataset_without_additive_sources_is_unchanged(dataset_dir):
+    """Omitting the additive-source args entirely must behave exactly like
+    the pre-merge SignatureDataset (no signet_/bhsig260_/gpds_ groups)."""
+    ds = SignatureDataset(dataset_dir)
+    groups = ds.signer_groups()
+    assert not any(g.startswith(("signet_", "bhsig260_", "gpds_")) for g in groups)
+
+
+def test_signet_discovery_tags_genuine_and_forged_correctly(dataset_dir, signet_dir):
+    ds = SignatureDataset(dataset_dir, signet_dir=signet_dir)
+    signet_records = [r for r in ds.discover() if r.signer_group.startswith("signet_")]
+    assert signet_records, "expected at least one SigNet record"
+    assert any(r.source_type == "genuine" for r in signet_records)
+    assert any(r.source_type == "forged" for r in signet_records)
+    assert all(r.script == "latin" for r in signet_records)
+    # A forged record's signer_group must match a genuine record's for the same writer id.
+    forged = next(r for r in signet_records if r.source_type == "forged")
+    assert any(r.signer_group == forged.signer_group and r.source_type == "genuine" for r in signet_records)
+
+
+def test_bhsig260_discovery_tags_script_and_label_correctly(dataset_dir, bhsig260_hindi_dir, bhsig260_bengali_dir):
+    ds = SignatureDataset(dataset_dir, bhsig260_hindi_dir=bhsig260_hindi_dir, bhsig260_bengali_dir=bhsig260_bengali_dir)
+    records = ds.discover()
+    hindi = [r for r in records if r.signer_group.startswith("bhsig260_hindi_")]
+    bengali = [r for r in records if r.signer_group.startswith("bhsig260_bengali_")]
+    assert hindi and bengali
+    assert all(r.script == "hindi" for r in hindi)
+    assert all(r.script == "bengali" for r in bengali)
+    assert any(r.source_type == "genuine" for r in hindi)
+    assert any(r.source_type == "forged" for r in hindi)
+
+
+def test_gpds_discovery_gracefully_skips_when_directory_absent(dataset_dir, gpds_dir):
+    """GPDS is deliberately not bundled (it was never wired into either
+    source pipeline's live path before this merge -- see README) -- passing
+    its (non-existent) path must not crash discovery, just contribute zero
+    records, same graceful-skip policy as any other missing source."""
+    assert not gpds_dir.exists()
+    ds = SignatureDataset(dataset_dir, gpds_dir=gpds_dir)
+    records = ds.discover()
+    assert not any(r.signer_group.startswith("gpds_") for r in records)
+    assert len(records) > 0  # the other sources still worked
+
+
+def test_three_additive_sources_combine_without_signer_group_collisions(
+    dataset_dir, signet_dir, bhsig260_hindi_dir, bhsig260_bengali_dir,
+):
+    ds = SignatureDataset(
+        dataset_dir, signet_dir=signet_dir,
+        bhsig260_hindi_dir=bhsig260_hindi_dir, bhsig260_bengali_dir=bhsig260_bengali_dir,
+    )
+    records = ds.discover()
+    prefixes = ("signer_", "signet_", "bhsig260_hindi_", "bhsig260_bengali_")
+    for r in records:
+        assert r.signer_group.startswith(prefixes), f"unexpected signer_group shape: {r.signer_group}"
+    # Every source actually contributed at least one group.
+    for prefix in ("signet_", "bhsig260_hindi_", "bhsig260_bengali_"):
+        assert any(g.startswith(prefix) for g in ds.signer_groups()), f"missing groups for {prefix}"
diff --git a/unified_pipeline/tests/test_end_to_end.py b/unified_pipeline/tests/test_end_to_end.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d3095b456d22537b2f9d47c3edfb3eba61a9975
--- /dev/null
+++ b/unified_pipeline/tests/test_end_to_end.py
@@ -0,0 +1,115 @@
+"""End-to-end integration tests running the full pipeline against the real bundled assets."""
+
+from __future__ import annotations
+
+import json
+
+import numpy as np
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+
+
+def _make_context(tmp_path, seed: int = 123):
+    overrides = [
+        f"paths.output_dir={tmp_path.as_posix()}",
+        "generation.count=1",
+    ]
+    config = load_config(overrides=[f"seed={seed}"] + overrides)
+    return config, GeneratorContext(config)
+
+
+def test_run_sample_produces_complete_metadata(tmp_path):
+    config, ctx = _make_context(tmp_path)
+    metadata, final_image, validation_report = ctx.run_sample(1)
+
+    assert isinstance(final_image, np.ndarray)
+    assert final_image.ndim == 3 and final_image.shape[2] == 3
+    assert final_image.dtype == np.uint8
+
+    for key in ("document_id", "template", "signer", "difficulty", "quality", "augmentation", "geometry", "metrics"):
+        assert key in metadata
+
+    assert validation_report is not None
+    assert validation_report.passed, [i.message for i in validation_report.issues]
+
+
+def test_run_sample_is_fully_reproducible(tmp_path):
+    config, ctx = _make_context(tmp_path, seed=555)
+    metadata1, image1, _ = ctx.run_sample(42)
+    metadata2, image2, _ = ctx.run_sample(42)
+
+    assert np.array_equal(image1, image2)
+    metadata1.pop("provenance", None)
+    metadata2.pop("provenance", None)
+    assert json.dumps(metadata1, sort_keys=True, default=str) == json.dumps(metadata2, sort_keys=True, default=str)
+
+
+def test_different_sample_indices_produce_different_documents(tmp_path):
+    config, ctx = _make_context(tmp_path, seed=555)
+    metadata1, image1, _ = ctx.run_sample(1)
+    metadata2, image2, _ = ctx.run_sample(2)
+
+    assert not np.array_equal(image1, image2) or metadata1["signer"] != metadata2["signer"]
+
+
+def test_difficulty_tier_is_one_of_expected_values(tmp_path):
+    config, ctx = _make_context(tmp_path, seed=7)
+    metadata, _, _ = ctx.run_sample(1)
+    assert metadata["difficulty"]["tier"] in ("easy", "medium", "hard")
+    assert 0.0 <= metadata["difficulty"]["score"] <= 1.0
+
+
+def test_generic_sample_on_a_multi_box_template_signs_every_box(tmp_path):
+    """A "generic" (no quality.case_type) sample is meant to represent a
+    normal, properly executed document -- if the template has more than one
+    signature box, every box should be genuinely signed, not just the one
+    box geometry/difficulty happens to be scored against. Leaving a second
+    box blank with no case-type/reject-code explaining why looked like a
+    broken render rather than a deliberate test case (see
+    src/generator.py::_run_generic_sample's "additional_signatures" handling)."""
+    config = load_config(overrides=[
+        f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1",
+        "document.template=bank_doc_09", "seed=3",
+    ])
+    ctx = GeneratorContext(config)
+    metadata, final_image, report = ctx.run_sample(1)
+
+    extras = metadata.get("additional_signatures")
+    assert extras and len(extras) == 1
+    assert extras[0]["box_id"] == "sig2"
+    assert extras[0]["signature_id"]
+    assert report is not None and report.passed
+
+
+def test_generic_sample_on_a_single_box_template_has_no_additional_signatures(tmp_path):
+    config = load_config(overrides=[
+        f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1",
+        "document.template=bank_doc_02", "seed=3",
+    ])
+    ctx = GeneratorContext(config)
+    metadata, final_image, report = ctx.run_sample(1)
+    assert "additional_signatures" not in metadata
+
+
+def test_cosmetic_fields_are_reproducible(tmp_path):
+    """Regression test: cosmetic_fields.py used to draw from Python's global
+    (unseeded) random module directly, so two identically-seeded runs could
+    render different NRIC/UEN/company-name/amount text -- invisible while
+    enable_cosmetic_fields defaulted to off, surfaced as a real
+    test_run_sample_is_fully_reproducible failure the moment it became
+    default-on. Forces a procedural template (guaranteed full cosmetic
+    field set, see src/cosmetics/cosmetic_fields.py) so this test actually
+    exercises the code path that broke."""
+    config = load_config(overrides=[
+        f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1",
+        "paths.template_provider=procedural", "document.template=proc_doc_04",
+        "document.enable_cosmetic_fields=true", "seed=91",
+    ])
+    ctx1 = GeneratorContext(config)
+    ctx2 = GeneratorContext(config)
+    metadata1, image1, _ = ctx1.run_sample(7)
+    metadata2, image2, _ = ctx2.run_sample(7)
+
+    assert np.array_equal(image1, image2)
+    assert metadata1["cosmetics"] == metadata2["cosmetics"]
diff --git a/unified_pipeline/tests/test_geometry_analyzer.py b/unified_pipeline/tests/test_geometry_analyzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..66bb725e41ebd42ac52c561bf979a217ba0c3ba3
--- /dev/null
+++ b/unified_pipeline/tests/test_geometry_analyzer.py
@@ -0,0 +1,94 @@
+"""Unit tests for src.analysis.geometry_analyzer."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from src.analysis.geometry_analyzer import GeometryAnalyzer
+
+
+def _blank_page(w: int = 400, h: int = 300) -> np.ndarray:
+    return np.full((h, w, 3), 250, dtype=np.uint8)
+
+
+def _draw_ink_blob(image: np.ndarray, box) -> np.ndarray:
+    out = image.copy()
+    x1, y1, x2, y2 = box
+    out[y1:y2, x1:x2] = 30  # dark ink blob
+    return out
+
+
+def test_geometry_analyzer_detects_ink_inside_target_box():
+    page = _blank_page()
+    target_box = (100, 100, 200, 150)
+    page = _draw_ink_blob(page, (120, 115, 180, 135))
+
+    analyzer = GeometryAnalyzer()
+    result = analyzer.analyze(page, target_box=target_box, canvas_size=(400, 300))
+
+    assert result.found is True
+    assert result.ink_pixels > 0
+    assert result.box_coverage_pct > 0
+    assert result.containment_pct > 0
+
+
+def test_geometry_analyzer_reports_not_found_on_blank_box():
+    page = _blank_page()
+    target_box = (100, 100, 200, 150)
+
+    analyzer = GeometryAnalyzer()
+    result = analyzer.analyze(page, target_box=target_box, canvas_size=(400, 300))
+
+    assert result.found is False
+    assert result.ink_pixels == 0
+    assert result.box_coverage_pct == 0.0
+    assert result.polygon_overlap_proxy == 0.0
+
+
+def test_geometry_analyzer_ignores_printed_rule_line():
+    """A thin, wide, solid horizontal line (a printed field rule) should not register as ink."""
+    page = _blank_page()
+    target_box = (50, 50, 350, 70)
+    page[64:66, 60:340] = 40  # printed underline: 2px tall, very wide, solid
+
+    analyzer = GeometryAnalyzer()
+    result = analyzer.analyze(page, target_box=target_box, canvas_size=(400, 300))
+
+    assert result.found is False
+
+
+def test_geometry_analyzer_full_coverage_signature():
+    page = _blank_page()
+    target_box = (100, 100, 200, 150)
+    page = _draw_ink_blob(page, (100, 100, 200, 150))  # fills the entire box
+
+    analyzer = GeometryAnalyzer()
+    result = analyzer.analyze(page, target_box=target_box, canvas_size=(400, 300))
+
+    assert result.found is True
+    assert result.box_coverage_pct > 95.0
+
+
+def test_geometry_analyzer_widens_search_with_placement_hint():
+    page = _blank_page()
+    target_box = (100, 100, 150, 120)
+    # Ink is well outside the tight target box (simulating a misplaced signature)
+    # but within the placement hint bbox used to widen the search.
+    hint_bbox = (250, 100, 320, 150)
+    page = _draw_ink_blob(page, (260, 110, 300, 140))
+
+    analyzer = GeometryAnalyzer()
+    result = analyzer.analyze(page, target_box=target_box, canvas_size=(400, 300), placement_hint_bbox=hint_bbox)
+
+    assert result.found is True
+    # Detected ink should be near the hint location, not the empty target box.
+    assert result.signature_bbox_px[0] > 200
+
+
+def test_geometry_result_no_nan_when_not_found():
+    import math
+    page = _blank_page()
+    analyzer = GeometryAnalyzer()
+    result = analyzer.analyze(page, target_box=(10, 10, 50, 30), canvas_size=(400, 300))
+    assert not math.isnan(result.mean_dark_intensity)
+    assert not math.isnan(result.gray_stddev)
diff --git a/unified_pipeline/tests/test_geometry_utils.py b/unified_pipeline/tests/test_geometry_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8e0f0e13381a5a790eb19c32cdda2bb34053f46
--- /dev/null
+++ b/unified_pipeline/tests/test_geometry_utils.py
@@ -0,0 +1,95 @@
+"""Unit tests for src.utils.geometry_utils."""
+
+from __future__ import annotations
+
+import math
+
+from src.utils.geometry_utils import (
+    box_center, box_to_polygon, clamp01, containment_pct, coverage_pct,
+    distance_between, iou, margin_utilization, points_to_polygon, safe_area,
+)
+
+
+def test_box_to_polygon_area():
+    poly = box_to_polygon((0, 0, 10, 20))
+    assert safe_area(poly) == 200.0
+
+
+def test_box_to_polygon_normalizes_reversed_coords():
+    poly = box_to_polygon((10, 20, 0, 0))
+    assert safe_area(poly) == 200.0
+
+
+def test_coverage_pct_full_overlap():
+    target = box_to_polygon((0, 0, 100, 100))
+    sig = box_to_polygon((0, 0, 100, 100))
+    assert coverage_pct(target, sig) == 100.0
+    assert containment_pct(target, sig) == 100.0
+    assert iou(target, sig) == 1.0
+
+
+def test_coverage_pct_partial_overlap():
+    target = box_to_polygon((0, 0, 100, 100))
+    sig = box_to_polygon((50, 0, 150, 100))
+    # Half of the target box is covered by the signature.
+    assert math.isclose(coverage_pct(target, sig), 50.0, abs_tol=1e-6)
+    # Half of the signature lies within the target box.
+    assert math.isclose(containment_pct(target, sig), 50.0, abs_tol=1e-6)
+
+
+def test_coverage_pct_no_overlap():
+    target = box_to_polygon((0, 0, 10, 10))
+    sig = box_to_polygon((100, 100, 110, 110))
+    assert coverage_pct(target, sig) == 0.0
+    assert containment_pct(target, sig) == 0.0
+    assert iou(target, sig) == 0.0
+
+
+def test_iou_partial():
+    a = box_to_polygon((0, 0, 10, 10))
+    b = box_to_polygon((5, 0, 15, 10))
+    # Intersection = 50, union = 100+100-50 = 150
+    assert math.isclose(iou(a, b), 50.0 / 150.0, rel_tol=1e-6)
+
+
+def test_points_to_polygon_handles_degenerate_input():
+    poly = points_to_polygon([])
+    assert poly.is_empty
+    poly_single = points_to_polygon([[5, 5]])
+    assert safe_area(poly_single) > 0
+
+
+def test_points_to_polygon_valid_shape():
+    poly = points_to_polygon([[0, 0], [10, 0], [10, 10], [0, 10]])
+    assert poly.is_valid
+    assert safe_area(poly) == 100.0
+
+
+def test_box_center_and_distance():
+    assert box_center((0, 0, 10, 20)) == (5.0, 10.0)
+    assert distance_between((0, 0), (3, 4)) == 5.0
+
+
+def test_distance_between_nan_safe():
+    assert math.isnan(distance_between((float("nan"), 0), (1, 1)))
+
+
+def test_clamp01():
+    assert clamp01(-1) == 0.0
+    assert clamp01(2) == 1.0
+    assert clamp01(0.5) == 0.5
+    assert clamp01(float("nan")) == 0.0
+
+
+def test_margin_utilization_centered_small_signature():
+    box = (0, 0, 100, 100)
+    sig = box_to_polygon((45, 45, 55, 55))
+    # A small centered signature uses very little of the available margin.
+    assert margin_utilization(box, sig) < 0.2
+
+
+def test_margin_utilization_full_box_signature():
+    box = (0, 0, 100, 100)
+    sig = box_to_polygon((0, 0, 100, 100))
+    # A signature filling the whole box reaches ~1.0 (corner at half-diagonal).
+    assert math.isclose(margin_utilization(box, sig), 1.0, rel_tol=1e-6)
diff --git a/unified_pipeline/tests/test_placement_engine.py b/unified_pipeline/tests/test_placement_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f05aeb28ed0972f0c4fa6eeb329fad7c7e722b7
--- /dev/null
+++ b/unified_pipeline/tests/test_placement_engine.py
@@ -0,0 +1,120 @@
+"""Unit tests for src.placement.placement_engine."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from src.config.schema import PlacementConfig
+from src.placement.placement_engine import PlacementEngine
+from src.utils.random_utils import SeedManager
+
+
+def _synthetic_signature(w: int = 300, h: int = 150) -> np.ndarray:
+    """A simple synthetic RGBA "signature": a diagonal opaque stroke on transparency."""
+    rgba = np.zeros((h, w, 4), dtype=np.uint8)
+    for x in range(w):
+        y = int((x / w) * (h - 1))
+        rgba[max(y - 3, 0):y + 3, x, :3] = (20, 20, 25)
+        rgba[max(y - 3, 0):y + 3, x, 3] = 255
+    return rgba
+
+
+def test_placement_is_deterministic_for_same_seed():
+    cfg = PlacementConfig(rotation_deg={"min": -5, "max": 5}, scale_factor={"min": 0.7, "max": 1.0})
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature()
+
+    r1 = engine.place(sig, target_box=(100, 100, 300, 200), canvas_size=(800, 600), seed_manager=SeedManager(1, 5))
+    r2 = engine.place(sig, target_box=(100, 100, 300, 200), canvas_size=(800, 600), seed_manager=SeedManager(1, 5))
+
+    assert r1.rotation_deg == r2.rotation_deg
+    assert r1.scale_factor == r2.scale_factor
+    assert r1.intended_bbox == r2.intended_bbox
+    assert np.array_equal(r1.layer, r2.layer)
+
+
+def test_placement_differs_for_different_sample_index():
+    cfg = PlacementConfig(rotation_deg={"min": -5, "max": 5}, scale_factor={"min": 0.7, "max": 1.0})
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature()
+
+    r1 = engine.place(sig, target_box=(100, 100, 300, 200), canvas_size=(800, 600), seed_manager=SeedManager(1, 1))
+    r2 = engine.place(sig, target_box=(100, 100, 300, 200), canvas_size=(800, 600), seed_manager=SeedManager(1, 2))
+
+    assert r1.rotation_deg != r2.rotation_deg or r1.scale_factor != r2.scale_factor
+
+
+def test_placement_respects_aspect_ratio_when_not_rotated():
+    cfg = PlacementConfig(rotation_deg=0.0, scale_factor=0.5, margin=0.0)
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature(w=300, h=150)  # aspect ratio 2:1
+
+    result = engine.place(sig, target_box=(0, 0, 400, 400), canvas_size=(800, 800), seed_manager=SeedManager(1, 1))
+    x1, y1, x2, y2 = result.intended_bbox
+    placed_w, placed_h = x2 - x1, y2 - y1
+    assert abs((placed_w / placed_h) - 2.0) < 0.05
+
+
+def test_placement_output_layer_matches_canvas_size():
+    cfg = PlacementConfig()
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature()
+    result = engine.place(sig, target_box=(10, 10, 200, 100), canvas_size=(640, 480), seed_manager=SeedManager(2, 0))
+    assert result.layer.shape == (480, 640, 4)
+
+
+def test_tiny_box_still_produces_a_legible_minimum_height_signature():
+    cfg = PlacementConfig(rotation_deg=0.0, scale_factor=1.0, margin=0.05)
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature(w=300, h=150)
+
+    # A box far shorter than the natural signature height.
+    result = engine.place(sig, target_box=(0, 0, 300, 6), canvas_size=(800, 800), seed_manager=SeedManager(1, 1))
+    x1, y1, x2, y2 = result.intended_bbox
+    assert (y2 - y1) >= cfg.min_signature_height_px - 1  # allow rounding
+
+
+def test_aggressive_downscale_onto_a_short_box_keeps_ink_dark():
+    """Regression test: a short "sign here" line (real bank forms are often
+    only ~15-25px tall) forces a large downscale of the signature stamp.
+    ``cv2.resize``'s INTER_AREA area-averaging dilutes a thin stroke's alpha
+    well below fully opaque during that downscale -- a signature that was
+    solidly inked in its source crop was coming out as a barely-visible
+    gray smudge (user-reported, traced to a real ``bank_doc_08`` case whose
+    Bank Manager signature box is only 22px tall). A gamma correction on
+    the placed alpha channel (strength self-scaling with how aggressive the
+    downscale was) compensates -- the placed ink must stay reasonably
+    opaque, not fade to a faint smudge, just because the box is short."""
+    cfg = PlacementConfig(rotation_deg=0.0, scale_factor=1.0, margin=0.05)
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature(w=600, h=300)  # large, fully-opaque (alpha=255) stroke
+
+    # A box short enough to force a large downscale (~0.07x), matching the
+    # real bug's scenario.
+    result = engine.place(sig, target_box=(0, 0, 600, 22), canvas_size=(1200, 1200), seed_manager=SeedManager(1, 1))
+    alpha = result.layer[:, :, 3]
+    nz = alpha[alpha > 0]
+    assert nz.size > 0
+    placed_alpha_p75 = np.percentile(nz, 75)
+    assert placed_alpha_p75 >= 180, (
+        f"placed ink faded to {placed_alpha_p75}/255 from a fully-opaque source -- "
+        "should stay reasonably dark even after a large downscale, not fade to a faint smudge"
+    )
+
+
+def test_misplacement_never_triggers_when_probability_zero():
+    cfg = PlacementConfig(misplacement_probability=0.0)
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature()
+    for i in range(20):
+        result = engine.place(sig, target_box=(50, 50, 250, 150), canvas_size=(800, 600), seed_manager=SeedManager(1, i))
+        assert result.misplaced is False
+
+
+def test_misplacement_always_triggers_when_probability_one():
+    cfg = PlacementConfig(misplacement_probability=1.0, misplacement_strength=0.5)
+    engine = PlacementEngine(cfg)
+    sig = _synthetic_signature()
+    for i in range(10):
+        result = engine.place(sig, target_box=(50, 50, 250, 150), canvas_size=(800, 600), seed_manager=SeedManager(1, i))
+        assert result.misplaced is True
diff --git a/unified_pipeline/tests/test_procedural_template_provider.py b/unified_pipeline/tests/test_procedural_template_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8e1169ec3021f1bff56c90154fcb287294d1e84
--- /dev/null
+++ b/unified_pipeline/tests/test_procedural_template_provider.py
@@ -0,0 +1,161 @@
+"""Tests for the matplotlib-drawn ProceduralTemplateProvider (ported from
+projectA/utils/templatemaker.py) and CompositeTemplateProvider, plus
+end-to-end smoke tests that the pipeline actually runs against both.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.templates.procedural_template_provider import TEMPLATE_CONFIGS, ProceduralTemplateProvider
+from src.templates.template_loader import TemplateLoader
+from src.templates.template_provider import CompositeTemplateProvider
+
+
+def test_discovers_all_fifteen_templates(tmp_path):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    templates = provider.discover()
+    assert len(templates) == len(TEMPLATE_CONFIGS) == 15
+    assert set(templates.keys()) == {f"proc_doc_{i:02d}" for i in range(1, 16)}
+
+
+def test_each_template_is_cached_to_a_real_png(tmp_path):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    for template_id, template in provider.discover().items():
+        assert template.image_path.exists(), template_id
+        assert template.image_path.stat().st_size > 0
+
+
+def test_box_count_matches_configured_signature_count(tmp_path):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    for i, cfg in enumerate(TEMPLATE_CONFIGS, start=1):
+        template = provider.get(f"proc_doc_{i:02d}")
+        boxes = template.annotation.pages[0].signature_boxes
+        # get_signature_boxes()'s cheque-voucher branch always draws exactly
+        # ONE signature line regardless of cfg["sigs"] (a single "AUTHORIZED
+        # SIGNATORY / DRAWER MANDATE" slot) -- see its docstring in
+        # src/templates/procedural_template_provider.py.
+        expected = 1 if cfg["type"].lower() == "cheque voucher" else cfg["sigs"]
+        assert len(boxes) == expected
+        for box in boxes:
+            assert box.x2 > box.x1 and box.y2 > box.y1
+            assert 0 <= box.x1 and box.x2 <= template.annotation.pages[0].page_size[0]
+            assert 0 <= box.y1 and box.y2 <= template.annotation.pages[0].page_size[1]
+
+
+def test_random_template_is_deterministic(tmp_path):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    t1 = provider.random_template(np.random.default_rng(9))
+    t2 = provider.random_template(np.random.default_rng(9))
+    assert t1.template_id == t2.template_id
+
+
+def test_get_unknown_id_raises(tmp_path):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    try:
+        provider.get("proc_doc_99")
+    except KeyError:
+        pass
+    else:
+        raise AssertionError("expected KeyError for an unregistered template id")
+
+
+def test_field_boxes_for_returns_the_matching_config(tmp_path):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    provider.discover()
+    cfg = provider.field_boxes_for("proc_doc_01")
+    assert cfg == TEMPLATE_CONFIGS[0]
+    assert provider.field_boxes_for("does_not_exist") is None
+
+
+def test_second_render_reuses_the_cached_file_without_rerendering(tmp_path, monkeypatch):
+    provider = ProceduralTemplateProvider(cache_dir=tmp_path)
+    provider.discover()
+    first_mtime = provider.get("proc_doc_01").image_path.stat().st_mtime_ns
+
+    provider2 = ProceduralTemplateProvider(cache_dir=tmp_path)
+    provider2.discover()
+    second_mtime = provider2.get("proc_doc_01").image_path.stat().st_mtime_ns
+    assert first_mtime == second_mtime  # never rewritten, just reused
+
+
+# --------------------------------------------------------------------- #
+# CompositeTemplateProvider
+# --------------------------------------------------------------------- #
+
+def test_composite_merges_scanned_and_procedural_without_collision(tmp_path, image_dir, annotations_dir):
+    scanned = TemplateLoader(image_dir, annotations_dir)
+    procedural = ProceduralTemplateProvider(cache_dir=tmp_path)
+    composite = CompositeTemplateProvider([scanned, procedural])
+
+    ids = set(composite.list_ids())
+    assert ids == set(scanned.list_ids()) | set(procedural.list_ids())
+    assert len(ids) == 9 + 15
+
+
+def test_composite_get_dispatches_to_the_owning_provider(tmp_path, image_dir, annotations_dir):
+    scanned = TemplateLoader(image_dir, annotations_dir)
+    procedural = ProceduralTemplateProvider(cache_dir=tmp_path)
+    composite = CompositeTemplateProvider([scanned, procedural])
+
+    assert composite.get("bank_doc_01").image_path == scanned.get("bank_doc_01").image_path
+    assert composite.get("proc_doc_01").image_path == procedural.get("proc_doc_01").image_path
+    assert composite.field_boxes_for("proc_doc_01") is not None
+    assert composite.field_boxes_for("bank_doc_01") is None
+
+
+# --------------------------------------------------------------------- #
+# End-to-end smoke tests through GeneratorContext
+# --------------------------------------------------------------------- #
+
+def _make_context(tmp_path, template_provider: str, seed: int = 321, enable_cosmetic_fields: bool = False):
+    overrides = [
+        f"seed={seed}",
+        f"paths.output_dir={tmp_path.as_posix()}",
+        f"paths.template_provider={template_provider}",
+        "generation.count=1",
+    ]
+    if enable_cosmetic_fields:
+        overrides.append("document.enable_cosmetic_fields=true")
+    config = load_config(overrides=overrides)
+    return GeneratorContext(config)
+
+
+def test_procedural_template_provider_end_to_end(tmp_path):
+    ctx = _make_context(tmp_path, "procedural")
+    metadata, final_image, validation_report = ctx.run_sample(1)
+
+    assert isinstance(final_image, np.ndarray)
+    assert final_image.ndim == 3 and final_image.shape[2] == 3
+    assert metadata["template"].startswith("proc_doc_")
+    assert validation_report is not None and validation_report.passed, (
+        [i.message for i in validation_report.issues] if validation_report else None
+    )
+
+
+def test_procedural_with_cosmetic_fields_enabled_end_to_end(tmp_path):
+    ctx = _make_context(tmp_path, "procedural", enable_cosmetic_fields=True)
+    metadata, final_image, validation_report = ctx.run_sample(1)
+
+    assert isinstance(final_image, np.ndarray)
+    assert "cosmetics" in metadata
+    assert metadata["cosmetics"]["field_boxes"]
+    assert validation_report is not None and validation_report.passed
+
+
+def test_both_template_providers_end_to_end(tmp_path):
+    """template_provider=both should be able to land on either a scanned
+    or procedural template across a few samples (deterministic given seed,
+    but not guaranteed which -- so just check every sample is valid and at
+    least the id namespace is one of the two known prefixes)."""
+    ctx = _make_context(tmp_path, "both")
+    seen_prefixes = set()
+    for i in range(1, 6):
+        metadata, final_image, validation_report = ctx.run_sample(i)
+        template_id = metadata["template"]
+        assert template_id.startswith(("bank_doc_", "proc_doc_"))
+        seen_prefixes.add(template_id.split("_0")[0])
+        assert validation_report is not None and validation_report.passed
+    assert seen_prefixes  # at least one provider was exercised
diff --git a/unified_pipeline/tests/test_random_utils.py b/unified_pipeline/tests/test_random_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e7a63e0fc0c6d6574a5dc7dcc5f272413ea2642
--- /dev/null
+++ b/unified_pipeline/tests/test_random_utils.py
@@ -0,0 +1,70 @@
+"""Unit tests for src.utils.random_utils -- the determinism backbone of the whole pipeline."""
+
+from __future__ import annotations
+
+import numpy as np
+
+from src.config.schema import Range
+from src.utils.random_utils import SeedManager, derive_seed, resolve_scalar_or_range
+
+
+def test_derive_seed_is_deterministic():
+    assert derive_seed(42, 1, "placement") == derive_seed(42, 1, "placement")
+
+
+def test_derive_seed_varies_with_each_part():
+    base = derive_seed(42, 1, "placement")
+    assert derive_seed(42, 2, "placement") != base
+    assert derive_seed(42, 1, "rendering") != base
+    assert derive_seed(43, 1, "placement") != base
+
+
+def test_derive_seed_in_valid_range():
+    seed = derive_seed(0, 999999, "x")
+    assert 0 <= seed < 2**32
+
+
+def test_seed_manager_same_sample_same_stage_is_deterministic():
+    sm1 = SeedManager(master_seed=7, sample_index=3)
+    sm2 = SeedManager(master_seed=7, sample_index=3)
+    draws1 = sm1.numpy_rng("placement").uniform(0, 1, size=10)
+    draws2 = sm2.numpy_rng("placement").uniform(0, 1, size=10)
+    assert np.allclose(draws1, draws2)
+
+
+def test_seed_manager_different_stages_are_independent():
+    sm = SeedManager(master_seed=7, sample_index=3)
+    a = sm.numpy_rng("placement").uniform(0, 1, size=5)
+    b = sm.numpy_rng("rendering").uniform(0, 1, size=5)
+    assert not np.allclose(a, b)
+
+
+def test_seed_manager_different_samples_are_independent():
+    a = SeedManager(master_seed=7, sample_index=1).numpy_rng("placement").uniform(0, 1, size=5)
+    b = SeedManager(master_seed=7, sample_index=2).numpy_rng("placement").uniform(0, 1, size=5)
+    assert not np.allclose(a, b)
+
+
+def test_resolve_scalar_or_range_with_scalar():
+    rng = np.random.default_rng(0)
+    assert resolve_scalar_or_range(0.5, rng) == 0.5
+
+
+def test_resolve_scalar_or_range_with_range_stays_in_bounds():
+    rng = np.random.default_rng(0)
+    r = Range(min=-3.0, max=5.0)
+    for _ in range(200):
+        v = resolve_scalar_or_range(r, rng)
+        assert -3.0 <= v <= 5.0
+
+
+def test_resolve_scalar_or_range_with_dict_range():
+    rng = np.random.default_rng(0)
+    v = resolve_scalar_or_range({"min": 1.0, "max": 1.0}, rng)
+    assert v == 1.0
+
+
+def test_resolve_scalar_or_range_degenerate_range_returns_bound():
+    rng = np.random.default_rng(0)
+    r = Range(min=2.0, max=2.0)
+    assert resolve_scalar_or_range(r, rng) == 2.0
diff --git a/unified_pipeline/tests/test_scanned_cosmetic_fields.py b/unified_pipeline/tests/test_scanned_cosmetic_fields.py
new file mode 100644
index 0000000000000000000000000000000000000000..be3f279e04feda575303093f73ed243487caf7bc
--- /dev/null
+++ b/unified_pipeline/tests/test_scanned_cosmetic_fields.py
@@ -0,0 +1,161 @@
+"""Tests for scanned-template cosmetic field filling and the reference-
+signature export helper (both added after a real bug was found: BHSig260
+.tif reference signatures were being renamed to *.png without actual format
+conversion, producing files real image viewers refused to open)."""
+
+from __future__ import annotations
+
+import random
+from pathlib import Path
+
+from PIL import Image
+
+from src.cosmetics.scanned_cosmetic_fields import fill_scanned_cosmetic_fields
+from src.generator import _export_reference_signature
+from src.templates.template_loader import TemplateLoader
+
+_FILLABLE_TEMPLATES = ["bank_doc_03", "bank_doc_04", "bank_doc_05", "bank_doc_06", "bank_doc_07", "bank_doc_08", "bank_doc_09"]
+
+
+def _cosmetic_fields(template):
+    return template.annotation.pages[0].cosmetic_fields
+
+
+def test_every_fillable_template_finds_and_fills_its_field(image_dir, annotations_dir):
+    """bank_doc_01/02 are stock photos with fabricated content baked in (no
+    genuine blank field) and are deliberately excluded from FIELD_REGION_RECIPES --
+    every other scanned template must have its configured field pre-measured
+    into its annotation and get it filled."""
+    loader = TemplateLoader(image_dir, annotations_dir)
+    for template_id in _FILLABLE_TEMPLATES:
+        template = loader.get(template_id)
+        assert _cosmetic_fields(template), f"{template_id}: annotation has no cosmetic_fields"
+        from src.utils.image_io import load_image_rgb
+        img = load_image_rgb(template.image_path)
+        result, field_boxes = fill_scanned_cosmetic_fields(img, _cosmetic_fields(template), "Jonathan Tan", random.Random(1))
+        assert field_boxes, f"{template_id}: no field was found/filled"
+        assert result.shape == img.shape
+        assert not (result == img).all(), f"{template_id}: image unchanged, nothing was actually drawn"
+
+
+def test_templates_without_configured_fields_are_untouched(image_dir, annotations_dir):
+    """bank_doc_01/bank_doc_02 (stock photos, no genuine blank field) must be a no-op."""
+    import numpy as np
+    loader = TemplateLoader(image_dir, annotations_dir)
+    for template_id in ["bank_doc_01", "bank_doc_02"]:
+        template = loader.get(template_id)
+        assert _cosmetic_fields(template) == []
+        from src.utils.image_io import load_image_rgb
+        img = load_image_rgb(template.image_path)
+        result, field_boxes = fill_scanned_cosmetic_fields(img, _cosmetic_fields(template), "Jonathan Tan", random.Random(1))
+        assert field_boxes == {}
+        assert np.array_equal(result, img)
+
+
+def test_company_name_field_gets_a_generated_company_name_not_the_signer(image_dir, annotations_dir):
+    """Regression test: bank_doc_09's "Company Name" table cell (under
+    ENTITY DETAILS, next to Registration Number) was getting the signer's
+    own personal name pasted into it (e.g. "Nancy Ivanov" under a column
+    literally headed "Company Name") -- right position, wrong kind of text.
+    Every OTHER scanned field really is a personal-name field ("Print
+    Name:", "Name of Account Holder:", "Mr./Ms. ___") and must still get
+    the signer's name, unaffected by this fix."""
+    from src.utils.image_io import load_image_rgb
+
+    loader = TemplateLoader(image_dir, annotations_dir)
+    template = loader.get("bank_doc_09")
+    img = load_image_rgb(template.image_path)
+
+    _, field_boxes = fill_scanned_cosmetic_fields(img, _cosmetic_fields(template), "Nancy Ivanov", random.Random(1))
+    assert "company_name" in field_boxes
+
+    # Can't OCR the rendered text back out of the image in a unit test, so
+    # assert on the generator function directly producing something that
+    # doesn't look like the personal name that used to leak through, and
+    # confirm the module actually calls it (not primary_person_name) via
+    # the _GENERATED_FIELD_TEXT dispatch table.
+    from src.cosmetics.scanned_cosmetic_fields import _GENERATED_FIELD_TEXT
+    assert "company_name" in _GENERATED_FIELD_TEXT
+    generated = _GENERATED_FIELD_TEXT["company_name"](random.Random(1))
+    assert generated != "Nancy Ivanov"
+    assert generated.endswith("Pte Ltd")
+
+
+def test_scanned_cosmetic_fields_are_reproducible(image_dir, annotations_dir):
+    from src.utils.image_io import load_image_rgb
+    import numpy as np
+
+    loader = TemplateLoader(image_dir, annotations_dir)
+    template = loader.get("bank_doc_09")
+    img = load_image_rgb(template.image_path)
+
+    result1, boxes1 = fill_scanned_cosmetic_fields(img, _cosmetic_fields(template), "Jonathan Tan", random.Random(42))
+    result2, boxes2 = fill_scanned_cosmetic_fields(img, _cosmetic_fields(template), "Jonathan Tan", random.Random(42))
+    assert np.array_equal(result1, result2)
+    assert boxes1 == boxes2
+
+
+def test_multi_signer_templates_have_a_box_id_tagged_field_per_signatory(image_dir, annotations_dir):
+    """Regression test: bank_doc_03/04/05/06/08 each have TWO signature
+    boxes, and (confirmed by visually cropping every candidate line in each
+    template) each box has its OWN "Print Name:"-style line -- not one
+    shared field. Before this fix, ``detect_cosmetic_field_coordinates.py``
+    only ever measured ONE such line per template, so the second
+    signatory's printed name was always left blank no matter what filled
+    the first. Every field belonging to one specific signatory must now
+    carry a ``box_id`` matching a real ``signature_boxes[].id`` on the same
+    page."""
+    loader = TemplateLoader(image_dir, annotations_dir)
+    for template_id in ["bank_doc_03", "bank_doc_04", "bank_doc_05", "bank_doc_06", "bank_doc_08"]:
+        template = loader.get(template_id)
+        fields = _cosmetic_fields(template)
+        box_ids = {b.id for b in template.annotation.pages[0].signature_boxes}
+        field_box_ids = {f.box_id for f in fields}
+        assert field_box_ids == box_ids, f"{template_id}: expected one field per signature box {box_ids}, got {field_box_ids}"
+
+
+def test_filling_only_one_signatorys_fields_leaves_the_others_blank(image_dir, annotations_dir):
+    """The generator fills a multi-signer template's cosmetic fields in two
+    passes -- one per signer, as each signer's identity becomes known (see
+    ``src/generator.py::_maybe_fill_cosmetic_fields``/``_fill_box_cosmetic_field``).
+    Passing only the fields for one ``box_id`` must fill only that
+    signatory's line, proving the two passes can't leak into each other's
+    box."""
+    from src.utils.image_io import load_image_rgb
+    import numpy as np
+
+    loader = TemplateLoader(image_dir, annotations_dir)
+    template = loader.get("bank_doc_04")
+    img = load_image_rgb(template.image_path)
+    all_fields = _cosmetic_fields(template)
+    sig1_fields = [f for f in all_fields if f.box_id == "sig1"]
+    sig2_fields = [f for f in all_fields if f.box_id == "sig2"]
+    assert sig1_fields and sig2_fields
+
+    only_sig1, boxes_sig1 = fill_scanned_cosmetic_fields(img, sig1_fields, "Ronald Thompson", random.Random(1))
+    assert set(boxes_sig1) == {"name_sig1"}
+
+    only_sig1_then_sig2, boxes_sig2 = fill_scanned_cosmetic_fields(only_sig1, sig2_fields, "George Dubois", random.Random(2))
+    assert set(boxes_sig2) == {"name_sig2"}
+    assert not np.array_equal(only_sig1_then_sig2, only_sig1), "second pass must actually draw something new"
+
+
+def test_export_reference_signature_produces_a_real_png_from_a_tiff_source(tmp_path):
+    """Regression test for the bug this session found: a previous version
+    just shutil.copy2'd the raw source bytes under a *_reference_signature.png
+    name, so a BHSig260 .tif source produced a file with TIFF bytes but a
+    .png extension -- any standards-compliant viewer refuses to open that
+    ("error occurred while loading image")."""
+    src = tmp_path / "source.tif"
+    Image.new("L", (40, 20), color=200).save(src, format="TIFF")
+
+    dst = tmp_path / "case_001_reference_signature.png"
+    _export_reference_signature(str(src), dst)
+
+    assert dst.exists()
+    with dst.open("rb") as f:
+        header = f.read(8)
+    assert header == b"\x89PNG\r\n\x1a\n", "output file's actual content must be a real PNG, not just named one"
+    with Image.open(dst) as im:
+        assert im.format == "PNG"
+        assert im.size == (40, 20)
diff --git a/unified_pipeline/tests/test_validation_engine.py b/unified_pipeline/tests/test_validation_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e37c227e58a1641a1b314ad900578c2159976d
--- /dev/null
+++ b/unified_pipeline/tests/test_validation_engine.py
@@ -0,0 +1,126 @@
+"""Unit tests for src.analysis.validation_engine."""
+
+from __future__ import annotations
+
+import copy
+
+from src.analysis.validation_engine import ValidationEngine, summarize
+from src.config.schema import DifficultyWeights, ValidationConfig
+
+VALID_METADATA = {
+    "document_id": "sample_000001",
+    "template": "bank_doc_01",
+    "page": 1,
+    "page_size": [1000, 800],
+    "signer": {"name": "Jane Doe", "role": "Account Holder"},
+    "signature_source": {"signature_id": "sig_0001", "path": "dataset/genuine/x.png"},
+    "placement": {"rotation_deg": 0.0},
+    "rendering": {"blend_mode": "multiply"},
+    "difficulty": {
+        "misplacement": 0.6, "whitespace": 0.5, "faintness": 0.5, "smallness": 0.5, "low_texture": 0.5,
+        "score": round(0.35 * 0.6 + 0.20 * 0.5 + 0.20 * 0.5 + 0.15 * 0.5 + 0.10 * 0.5, 4),
+        "tier": "medium",
+    },
+    "quality": {"degraded": False},
+    "verification": {
+        "case_type": None, "verdict": None, "reject_code": None,
+        "target_box_id": None, "expected_signer_group": None, "expected_signer_script": None,
+        "actual_signer_group": None, "actual_source_type": None, "actual_signer_script": None,
+        "companion": None,
+    },
+    "augmentation": {},
+    "geometry": {
+        "detected": True,
+        "ink_pixels": 120,
+        "target_box": [100, 100, 200, 150],
+        "actual_box": [110, 105, 190, 145],
+        "signature_bbox_px": [110, 105, 190, 145],
+        "box_px": [100, 100, 200, 150],
+        "signature_polygon_px": [[110, 105], [190, 105], [190, 145], [110, 145]],
+    },
+    "metrics": {"box_coverage_pct": 80.0},
+}
+
+
+def _engine(strict: bool = False) -> ValidationEngine:
+    return ValidationEngine(ValidationConfig(strict=strict), DifficultyWeights())
+
+
+def test_valid_metadata_passes():
+    report = _engine().validate(copy.deepcopy(VALID_METADATA))
+    assert report.passed, [i.message for i in report.issues]
+    assert not report.errors
+
+
+def test_missing_top_level_key_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    del bad["geometry"]
+    report = _engine().validate(bad)
+    assert not report.passed
+    assert any("geometry" in i.field for i in report.errors)
+
+
+def test_signature_not_detected_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["geometry"]["detected"] = False
+    report = _engine().validate(bad)
+    assert not report.passed
+
+
+def test_degenerate_polygon_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["geometry"]["signature_polygon_px"] = [[0, 0], [1, 1]]
+    report = _engine().validate(bad)
+    assert not report.passed
+
+
+def test_invalid_box_ordering_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["geometry"]["target_box"] = [200, 100, 100, 150]  # x2 < x1
+    report = _engine().validate(bad)
+    assert not report.passed
+
+
+def test_box_outside_page_is_warning_not_error_by_default():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["geometry"]["target_box"] = [900, 700, 1200, 900]  # exceeds page_size [1000, 800]
+    report = _engine(strict=False).validate(bad)
+    assert report.passed  # warning only, not strict
+    assert report.warnings
+
+    report_strict = _engine(strict=True).validate(bad)
+    assert not report_strict.passed
+
+
+def test_nan_value_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["metrics"]["box_coverage_pct"] = float("nan")
+    report = _engine().validate(bad)
+    assert not report.passed
+
+
+def test_difficulty_score_mismatch_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["difficulty"]["score"] = 0.999  # inconsistent with components
+    report = _engine().validate(bad)
+    assert not report.passed
+
+
+def test_difficulty_tier_mismatch_fails():
+    bad = copy.deepcopy(VALID_METADATA)
+    bad["difficulty"]["tier"] = "hard"  # inconsistent with score ~0.49 -> medium
+    report = _engine().validate(bad)
+    assert not report.passed
+
+
+def test_summarize_batch():
+    good = _engine().validate(copy.deepcopy(VALID_METADATA))
+    bad_meta = copy.deepcopy(VALID_METADATA)
+    bad_meta["geometry"]["detected"] = False
+    bad = _engine().validate(bad_meta)
+
+    summary = summarize([good, bad])
+    assert summary.total == 2
+    assert summary.passed == 1
+    assert summary.failed == 1
+    assert "sample_000001" in summary.failed_sample_ids
diff --git a/unified_pipeline/tests/test_verification_cases.py b/unified_pipeline/tests/test_verification_cases.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8922e0192746bb81e7fc879b3b7f410afcf46c8
--- /dev/null
+++ b/unified_pipeline/tests/test_verification_cases.py
@@ -0,0 +1,294 @@
+"""Tests for the case_type-driven verification test-case generation feature.
+
+Covers: registry determinism, correct rendering/ground-truth for every one
+of the 12 case types, companion-box behavior, and backward compatibility
+with the existing (non-case-type) generation path.
+"""
+
+from __future__ import annotations
+
+from src.config.loader import load_config
+from src.generator import GeneratorContext
+from src.verification.case_types import CASE_TABLE
+from src.verification.registry import SignerIdentityRegistry
+
+
+def _make_context(tmp_path, case_type: "str | None" = None, seed: int = 4242, document_template: "str | None" = None):
+    overrides = [f"seed={seed}", f"paths.output_dir={tmp_path.as_posix()}", "generation.count=1"]
+    if case_type is not None:
+        overrides.append(f"quality.case_type={case_type}")
+    if document_template is not None:
+        overrides.append(f"document.template={document_template}")
+    config = load_config(overrides=overrides)
+    return GeneratorContext(config)
+
+
+# --------------------------------------------------------------------- #
+# Registry
+# --------------------------------------------------------------------- #
+
+def test_registry_authorized_signer_is_deterministic(tmp_path):
+    ctx1 = _make_context(tmp_path)
+    ctx2 = _make_context(tmp_path)
+
+    for template_id, template in ctx1.template_provider.discover().items():
+        for box in template.annotation.pages[0].signature_boxes:
+            a = ctx1.identity_registry.authorized_signer_group(template_id, box.id)
+            b = ctx2.identity_registry.authorized_signer_group(template_id, box.id)
+            assert a == b
+
+
+def test_registry_display_name_is_stable_across_instances(tmp_path):
+    ctx1 = _make_context(tmp_path)
+    ctx2 = _make_context(tmp_path)
+    group = ctx1.identity_registry._signer_groups[0]
+    assert ctx1.identity_registry.display_name(group) == ctx2.identity_registry.display_name(group)
+
+
+def test_registry_foreign_authorized_picks_a_different_template(tmp_path):
+    ctx = _make_context(tmp_path)
+    group, foreign_template_id, foreign_box_id = ctx.identity_registry.foreign_authorized("bank_doc_01")
+    assert foreign_template_id != "bank_doc_01"
+    assert group == ctx.identity_registry.authorized_signer_group(foreign_template_id, foreign_box_id)
+
+
+# --------------------------------------------------------------------- #
+# Per-case-type ground truth
+# --------------------------------------------------------------------- #
+
+def test_all_case_types_produce_matching_verdict_and_reject_code(tmp_path):
+    for code, spec in CASE_TABLE.items():
+        ctx = _make_context(tmp_path, case_type=code, seed=99)
+        metadata, _, report = ctx.run_sample(1)
+        v = metadata["verification"]
+
+        assert v["case_type"] == code
+        assert v["verdict"] == spec.verdict, f"{code}: verdict mismatch"
+        assert v["reject_code"] == spec.reject_code, f"{code}: reject_code mismatch"
+        assert report is not None and report.passed, (
+            f"{code}: validation failed: {[i.message for i in report.errors]}"
+        )
+
+
+def test_pass_cases_use_the_authorized_signer(tmp_path):
+    for code in ("1a", "1b", "1d"):
+        ctx = _make_context(tmp_path, case_type=code, seed=17)
+        metadata, _, _ = ctx.run_sample(1)
+        v = metadata["verification"]
+        assert v["actual_signer_group"] == v["expected_signer_group"]
+        assert v["actual_source_type"] == "genuine"
+        assert v["companion"] is None
+
+
+def test_multi_signer_pass_case_fills_companion_correctly(tmp_path):
+    ctx = _make_context(tmp_path, case_type="1c", seed=17)
+    metadata, _, _ = ctx.run_sample(1)
+    v = metadata["verification"]
+    assert v["actual_signer_group"] == v["expected_signer_group"]
+    assert v["companion"] is not None
+    assert v["companion"]["filled"] is True
+    assert v["companion"]["matches_expected"] is True
+
+
+def test_multi_signer_pass_case_fills_both_print_name_fields(tmp_path):
+    """Regression test: bank_doc_04 has a BORROWER and a LENDER signature
+    box, each with its own "Print Name:" line. Before ``box_id`` was added
+    to ``CosmeticFieldAnnotation``, only the box passed to
+    ``_maybe_fill_cosmetic_fields`` (the case's target box) ever got its
+    name field filled -- the companion's own "Print Name:" line stayed
+    blank even though the companion signature itself was correctly
+    pasted in (see ``_fill_box_cosmetic_field``, which runs a second pass
+    once the companion's identity is resolved)."""
+    ctx = _make_context(tmp_path, case_type="1c", seed=17, document_template="bank_doc_04")
+    metadata, _, _ = ctx.run_sample(1)
+    field_boxes = metadata["cosmetics"]["field_boxes"]
+    assert {"name_sig1", "name_sig2"} <= set(field_boxes)
+
+
+def test_generic_sample_on_a_two_box_scanned_template_fills_both_print_name_fields(tmp_path):
+    """Same regression as above, but for the generic (no ``quality.case_type``)
+    path -- ``_run_generic_sample``'s "fill every other box too" logic must
+    also fill that other box's own cosmetic field, not just paste its
+    signature ink."""
+    ctx = _make_context(tmp_path, case_type=None, seed=17, document_template="bank_doc_04")
+    metadata, _, _ = ctx.run_sample(1)
+    field_boxes = metadata["cosmetics"]["field_boxes"]
+    assert {"name_sig1", "name_sig2"} <= set(field_boxes)
+
+
+def test_blank_cases_have_no_actual_signer(tmp_path):
+    for code in ("2a", "5a"):
+        ctx = _make_context(tmp_path, case_type=code, seed=5)
+        metadata, _, _ = ctx.run_sample(1)
+        v = metadata["verification"]
+        assert v["actual_signer_group"] is None
+        assert v["actual_source_type"] == "none"
+        assert metadata["signature_source"]["signature_id"] is None
+
+    ctx_5a = _make_context(tmp_path, case_type="5a", seed=5)
+    metadata_5a, _, _ = ctx_5a.run_sample(1)
+    assert metadata_5a["verification"]["companion"]["filled"] is True
+
+    ctx_2a = _make_context(tmp_path, case_type="2a", seed=5)
+    metadata_2a, _, _ = ctx_2a.run_sample(1)
+    assert metadata_2a["verification"]["companion"] is None
+
+
+def test_identity_mismatch_cases_use_a_different_signer(tmp_path):
+    for code in ("3a", "5b"):
+        ctx = _make_context(tmp_path, case_type=code, seed=23)
+        metadata, _, _ = ctx.run_sample(1)
+        v = metadata["verification"]
+        assert v["actual_signer_group"] != v["expected_signer_group"]
+        assert v["actual_source_type"] == "genuine"
+
+
+def test_skilled_forgery_case_uses_forged_sample_of_the_authorized_signer(tmp_path):
+    ctx = _make_context(tmp_path, case_type="3b", seed=23)
+    metadata, _, _ = ctx.run_sample(1)
+    v = metadata["verification"]
+    assert v["actual_signer_group"] == v["expected_signer_group"]
+    assert v["actual_source_type"] == "forged"
+
+
+def test_role_mismatch_cases_use_a_legitimately_registered_signer(tmp_path):
+    ctx = _make_context(tmp_path, case_type="4a", seed=31)
+    metadata, _, _ = ctx.run_sample(1)
+    v = metadata["verification"]
+    # The target box got the *companion* box's own authorized signer.
+    assert v["actual_signer_group"] == v["companion"]["signer_group"]
+    assert v["actual_signer_group"] != v["expected_signer_group"]
+
+    ctx_4b = _make_context(tmp_path, case_type="4b", seed=31)
+    metadata_4b, _, _ = ctx_4b.run_sample(1)
+    v4b = metadata_4b["verification"]
+    assert v4b["actual_signer_group"] != v4b["expected_signer_group"]
+
+
+def test_printed_text_case_has_no_biometric_identity(tmp_path):
+    ctx = _make_context(tmp_path, case_type="2b", seed=11)
+    metadata, _, _ = ctx.run_sample(1)
+    v = metadata["verification"]
+    assert v["actual_signer_group"] is None
+    assert v["actual_source_type"] == "printed"
+
+
+# --------------------------------------------------------------------- #
+# Backward compatibility
+# --------------------------------------------------------------------- #
+
+def test_unset_case_type_is_unaffected(tmp_path):
+    ctx = _make_context(tmp_path, case_type=None, seed=1)
+    metadata, _, report = ctx.run_sample(1)
+    v = metadata["verification"]
+    assert v == {
+        "case_type": None, "verdict": None, "reject_code": None,
+        "target_box_id": None, "expected_signer_group": None, "expected_signer_script": None,
+        "actual_signer_group": None, "actual_source_type": None, "actual_signer_script": None,
+        "companion": None,
+    }
+    assert report is not None and report.passed
+
+
+# --------------------------------------------------------------------- #
+# Multi-script signer pool (Hindi/Bengali/Latin named identities)
+# --------------------------------------------------------------------- #
+
+def test_display_name_passes_through_real_names_but_generates_for_anonymous_groups(tmp_path):
+    ctx = _make_context(tmp_path)
+    assert ctx.identity_registry.display_name("Priya Sharma") == "Priya Sharma"
+    generated = ctx.identity_registry.display_name("signer_0014")
+    assert generated != "signer_0014"
+    assert " " in generated  # looks like "First Last"
+
+
+def test_non_latin_signers_are_part_of_the_authorized_pool(tmp_path):
+    """BHSig260 Hindi/Bengali signers (bundled under data/raw/bhsig260_hindi
+    and data/raw/bhsig260_bengali -- see src/signatures/signature_dataset.py)
+    must be reachable as authorized signers, not just the anonymous CEDAR
+    pool. This supersedes an older version of this test that checked for
+    Venket's 7 hand-picked metadata.csv names (Priya Sharma etc.) -- that
+    11-file demo overlay was dropped as redundant with the much larger
+    bundled BHSig260 archives (see unified_pipeline/README.md); the
+    underlying capability being tested (multi-script signer availability)
+    is unchanged, just exercised against real bundled data instead."""
+    ctx = _make_context(tmp_path)
+    groups = ctx.signature_dataset.signer_groups()
+    assert any(g.startswith("bhsig260_hindi_") for g in groups)
+    assert any(g.startswith("bhsig260_bengali_") for g in groups)
+
+
+def test_non_latin_signer_flows_through_to_metadata_with_a_generated_name(tmp_path):
+    """Find the (template, box) pair whose authorized signer is a non-latin
+    (BHSig260) group, force that template, and check it renders cleanly with
+    a generated display name and the correct script recorded.
+
+    Not a random search over sample indices: `authorized_signer_group()` is
+    a pure function of `(master_seed, template_id, box_id)` only (see
+    src/verification/registry.py), so for a fixed seed there are only as
+    many distinct outcomes as there are (template, box) pairs (~15-18) no
+    matter how many sample indices are tried -- this enumerates that finite
+    outcome space directly instead of hoping a lucky seed/sample-index hits
+    one, which is what made an earlier version of this test flaky once
+    SignatureDataset's group composition changed.
+    """
+    seed = None
+    target_template_id = None
+    for candidate_seed in range(1, 51):
+        probe = _make_context(tmp_path, case_type="1a", seed=candidate_seed)
+        for template_id, template in probe.template_provider.discover().items():
+            for box in template.annotation.pages[0].signature_boxes:
+                group = probe.identity_registry.authorized_signer_group(template_id, box.id)
+                if group.startswith(("bhsig260_hindi_", "bhsig260_bengali_")):
+                    seed, target_template_id = candidate_seed, template_id
+                    break
+            if target_template_id:
+                break
+        if target_template_id:
+            break
+    if target_template_id is None:
+        raise AssertionError("No (template, box) pair maps to a BHSig260 signer for seeds 1-50.")
+
+    ctx = _make_context(tmp_path, case_type="1a", seed=seed, document_template=target_template_id)
+    metadata, _, report = ctx.run_sample(1)
+    v = metadata["verification"]
+    assert v["expected_signer_group"].startswith(("bhsig260_hindi_", "bhsig260_bengali_"))
+    # Auto-derived groups (see registry.py's _AUTO_GROUP_PATTERN) get a
+    # generated display name, never the raw group id itself.
+    assert metadata["signer"]["name"] != v["expected_signer_group"]
+    assert " " in metadata["signer"]["name"]
+    assert v["actual_signer_script"] == v["expected_signer_script"]
+    assert v["expected_signer_script"] != "latin"
+    assert report is not None and report.passed
+
+
+def test_forged_mode_degrades_gracefully_for_a_group_without_forgeries(tmp_path):
+    """A signer group with only genuine records (no forged counterpart) --
+    sampling 'forged' for them should fall back to their genuine records
+    rather than crash (SignatureDataset already logs a warning for this;
+    this just confirms the caller-facing behavior). Built as a small
+    synthetic dataset rather than relying on a specific real group
+    happening to lack forgeries, since every bundled source (CEDAR, SigNet,
+    BHSig260) pairs genuine+forged per writer by construction."""
+    from src.signatures.signature_dataset import SignatureDataset
+
+    data_dir = tmp_path / "genuine_only_dataset"
+    (data_dir / "solo").mkdir(parents=True)
+    (data_dir / "solo" / "sample_01.png").write_bytes(b"\x89PNG\r\n\x1a\n")
+    (data_dir / "metadata.csv").write_text(
+        "path,signer,source_type\nsolo/sample_01.png,Solo Signer,genuine\n", encoding="utf-8",
+    )
+    ds = SignatureDataset(data_dir)
+    records = ds.records_for_group("Solo Signer", source_filter="forged")
+    assert len(records) > 0
+    assert records[0].source_type == "genuine"
+    assert all(r.signer_group == "Solo Signer" for r in records)
+
+
+def test_unrecognized_case_type_label_falls_back_to_generic_path(tmp_path):
+    """A free-text case_type not in CASE_TABLE behaves exactly like today's plain-label usage."""
+    ctx = _make_context(tmp_path, case_type="my-custom-label", seed=1)
+    metadata, _, report = ctx.run_sample(1)
+    assert metadata["verification"]["case_type"] is None
+    assert metadata["quality"]["case_type"] == "my-custom-label"
+    assert report is not None and report.passed