Create readme.md

readme.md

@@ -0,0 +1,198 @@
+# Hindi (Devanagari) Text Rendering — Pilot Dataset
+
+Synthetic image/text pairs for training a diffusion model to render **Hindi text
+in Devanagari script**. 4,000 samples at 512x512, with typographically correct
+shaping, exact ground-truth transcriptions, and per-sample glyph masks.
+
+Built as a feasibility pilot for a Stable Diffusion 1.5 LoRA. Sized to answer one
+question cheaply — *can the model learn Devanagari glyph shapes at all?* — rather
+than to train a production model.
+
+---
+
+## Why this exists
+
+Most text-rendering datasets are Latin-only, and most Devanagari datasets are
+built for **recognition** (OCR), not **generation**. Those are not
+interchangeable: recognition sets are deliberately adversarial — blurred,
+occluded, low-resolution — and a generative model trained on them learns to
+produce blurry, occluded text.
+
+This dataset is the opposite: clean, sharp, and constrained so that every sample
+is something a generator should aim to reproduce.
+
+## What makes Devanagari hard
+
+- **Shirorekha** — a continuous horizontal bar joins letters across a word.
+  Models trained on Latin produce broken or wavy bars.
+- **Conjuncts** — क् + ष → क्ष. Hundreds of ligatures whose shapes are not
+  compositional from their parts.
+- **Reordering** — the matra ि is written *before* the consonant it follows
+  logically.
+- **Three vertical zones** — matras sit above and below the base line, so glyph
+  extent is far taller than Latin.
+- **Fine diacritics** — nukta ़, anusvara ं, chandrabindu ँ are 2–8 pixel
+  features that vanish under aggressive downsampling.
+
+## Contents
+
+```
+data/pilot/
+  images/          4,000 x 512x512 JPEG (quality 95)
+  masks/           4,000 x 512x512 PNG, 8-bit glyph coverage masks
+  metadata.jsonl   one record per sample
+  train_words.txt  72 words used in the images
+  holdout_words.txt 20 words deliberately NOT rendered
+fonts/             22 OFL Devanagari fonts
+corpus/            larger word lists (see Provenance)
+src/               the generator, so results are reproducible
+```
+
+### `metadata.jsonl` schema
+
+| field | type | meaning |
+|---|---|---|
+| `file` | str | path relative to `data/pilot/` |
+| `text` | str | the Hindi string rendered — exact ground truth |
+| `caption` | str | training caption (varied template) |
+| `tokens` | int | CLIP token count of the caption, template included |
+| `font` | str | filename in `fonts/` |
+| `em` | int | font em size in pixels |
+| `x`, `y` | int | top-left of the text box |
+| `w`, `h` | int | text box size in pixels |
+| `glyphs` | int | shaped glyph count (≠ character count, due to ligatures) |
+
+```json
+{"file": "images/000000.jpg", "text": "रुकें",
+ "caption": "Hindi text \"रुकें\" on a board, flat design", "tokens": 18,
+ "font": "Khand-Regular.ttf", "em": 40, "x": 150, "y": 405,
+ "w": 62, "h": 48, "glyphs": 3}
+```
+
+## Statistics
+
+| | |
+|---|---|
+| samples | 4,000 |
+| resolution | 512 x 512 |
+| unique words | 72 train (+20 held out, never rendered) |
+| fonts | 22, evenly sampled (156–211 samples each) |
+| font em size | 32–120 px, mean 75.5 |
+| text box | 44–454 px wide, 30–161 px tall |
+| caption tokens | 11–29 of CLIP's 77 |
+| glyphs per sample | 2–8 |
+
+Word vocabulary spans four groups: simple no-conjunct words, real signage
+vocabulary, conjunct-bearing words (क्ष ज्ञ त्र श्र द्व ह्म …), and diacritic
+stress cases (nukta, anusvara, chandrabindu, visarga).
+
+## How it was generated
+
+1. **Shaping** — text is shaped with **HarfBuzz** (`uharfbuzz`) and rasterised
+   per-glyph with **FreeType** (`freetype-py`).
+
+   > This matters more than it sounds. Pillow's published wheels ship **without
+   > Raqm**, so `ImageDraw.text()` renders Devanagari *unshaped*: conjuncts break
+   > into separate letters and pre-base ि lands on the wrong side of its
+   > consonant. The output looks plausible to a non-reader while being wrong.
+   > Any dataset built that way teaches a model incorrect letterforms.
+
+   Verified: क्ष / त्र / ज्ञ each collapse 3 codepoints → 1 glyph; ि reorders
+   ahead of its consonant; स्त्री → 2 glyphs.
+
+2. **Composition** — flat, gradient, or lightly textured backgrounds; randomised
+   font, size, position and colour, with foreground/background luminance
+   contrast **enforced** (≥ 0.35), not sampled and hoped for.
+
+3. **Captions** — 10 templates × 12 surface nouns, with the quoted Hindi string
+   as the single invariant anchor. The phrase "Hindi text" is dropped in ~20% of
+   samples so a model does not become dependent on a trigger phrase.
+
+## The 32px rule
+
+**Every sample renders text at font em size ≥ 32px, asserted at generation
+time.** This is not a stylistic choice. Stable Diffusion 1.5's 4-channel VAE
+downsamples 8×, and measured round-trips show Devanagari diacritics are
+destroyed below ~24px em and clean at 32px:
+
+| font em size | VAE reconstruction |
+|---|---|
+| 12–20 px | destroyed — nuktas gone, conjuncts illegible |
+| 24 px | marginal — nukta merges into the stroke |
+| **32 px** | **clean — minimum safe size** |
+| 48–64 px | near-perfect |
+
+Note this is **em size**, not ink-box height: measured ink/em ratio varies
+0.67–1.08 depending on which matras a word carries, so ink height is
+content-dependent and unsuitable as a threshold.
+
+Consequence: this dataset covers **headline- and signage-sized text**, roughly
+1–4 words. It is not suitable for training small or body-sized text rendering on
+a 4-channel VAE at 512px — that is a reconstruction limit no training run can
+overcome.
+
+## Intended use
+
+- Training / fine-tuning diffusion models (LoRA or full) for Devanagari text
+- Evaluating text-rendering quality, with exact transcriptions as ground truth
+- Glyph-conditioned approaches (ControlNet, GlyphControl, AnyText) — masks are
+  included for this
+- The 20 held-out words test generalisation rather than memorisation
+
+**Not suitable for:** OCR / scene-text recognition training (these are clean
+synthetic renders, not photographs), or small-text rendering (see above).
+
+## Limitations
+
+- **Synthetic only.** No photographic backgrounds, perspective, occlusion or
+  real-world lighting. A model trained solely on this will render clean text but
+  may not composite convincingly into photographs.
+- **Small vocabulary.** 72 words is a pilot scale, chosen to make the
+  feasibility question cheap to answer. It is far too small to teach the script
+  broadly.
+- **Conjunct coverage is partial.** श्च has zero coverage in the rendered
+  samples; a larger stratified corpus is included separately.
+- **One script, one language.** Devanagari as used for Hindi. Marathi and Nepali
+  share the script with minor differences and are not specifically covered.
+
+## Provenance and licensing
+
+Three components with different origins — worth separating:
+
+1. **Images and masks** — generated by the included code from a hand-written word
+   list (`corpus/pilot_words.txt`) using OFL fonts. No third-party data.
+   Released under **CC BY 4.0**.
+
+2. **Fonts** (`fonts/`) — 22 families from Google Fonts, all under the **SIL Open
+   Font License 1.1**, which permits embedding and redistribution. Includes Noto
+   Sans/Serif Devanagari, Mukta, Hind, Halant, Tiro Devanagari Hindi, Baloo 2,
+   Kalam, Khand, Martel, Modak, Palanquin, Rozha One, Sahitya, Teko, Yatra One,
+   Amita, Biryani, Poppins. All audited for full Devanagari coverage and genuine
+   conjunct formation.
+
+3. **`corpus/corpus.json`** — the larger 4,569-word stratified vocabulary is
+   **derived from transcriptions in IIIT-IndicSTR-Word** (words only; no images
+   from that dataset are included or redistributed here).
+
+
+If you use the corpus component, cite the source dataset:
+
+```bibtex
+@inproceedings{mondal2024indic,
+  title={Indic Scene Text on the Roadside},
+  author={Mondal, Ajoy and Tulsyan, Krishna and Jawahar, CV},
+  booktitle={International Conference on Document Analysis and Recognition},
+  pages={263--278}, year={2024}, organization={Springer}
+}
+```
+
+## Reproducing
+
+```bash
+python src/diag/font_audit.py                      # verify fonts
+python src/data/gen_pilot.py --n 4000 --masks --seed 42
+python src/diag/verify_shaping.py                  # confirm shaping is active
+```
+
+`PLAN.md` and `FINDINGS.md` document the full approach and every measurement
+quoted above.