ScribeRec release-03 (ONNX)

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README.md

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+---
+license: agpl-3.0
+library_name: onnx
+pipeline_tag: image-to-text
+language:
+- en
+tags:
+- ocr
+- text-recognition
+- onnx
+- ryzen-ai
+- xdna
+- npu
+- xint8
+- int8
+---
+
+# ScribeRec: a screen-text line recognizer for AMD's XDNA1 NPU
+
+ScribeRec is [ignite-scribe](https://github.com/jdominick05/ignite-scribe)'s text-line recognizer. It was designed
+and trained from scratch to run on the int8 engine of [ignite-xdna](https://github.com/jdominick05/ignite-xdna) on
+AMD's XDNA1 NPU (Phoenix and Hawk Point). It has 1.25 M parameters: a convolutional backbone that runs on the NPU,
+then a BiLSTM tail and CTC decoding on the CPU. It reads one line of screen text (UI, terminal, editor) at a time.
+
+This is **release-03**, fine-tuned from release-01 on the geometry of terminal-line crops.
+
+## Files (`r03/`)
+
+| File | What |
+|---|---|
+| `backbone_{320,640,960,1280}.onnx` | float backbone per input-width bucket (input height 80) |
+| `backbone_{320,640,960,1280}_xint8.onnx` | AMD Quark XINT8 QDQ versions, calibrated per bucket; what the NPU runs |
+| `backbone.onnx`, `backbone_xint8.onnx` | the 320 bucket under its old name |
+| `tail.onnx` | BiLSTM tail and classifier (dynamic n and t) |
+| `dict.txt` | the characters (436), PP-OCRv5's English dictionary |
+| `MODEL.md` | the training notes: architecture, classes, preprocessing, recipe, results |
+
+**Classes:** 438. Blank = 0; `dict.txt` = 1..436 in file order; space = 437. Preprocessing and decoding are in
+`MODEL.md` and in ignite-scribe's `scriberec_rec.py`.
+
+The compiled ignite-xdna NPU containers (`scriberec_<W>.ignite`) are not included yet. The XINT8 ONNX files are
+their exact source: the containers are bit-exact to them.
+
+## Results
+
+| Test | ScribeRec r03 | PP-OCRv5 (CPU) | ScribeRec release-01 |
+|---|---:|---:|---:|
+| Real Windows terminal, static text, character error rate | **0.22%** | 0.48-0.54% | 1.40-1.55% |
+| Live terminal UI full of box-drawing rules and spinners | 4.47% | 1.53% | 8.29% |
+| Synthetic UI lines (seed 100), XINT8 | 0.154% | | |
+
+- **Real-window numbers:** ignite-scribe's `tools/ocr_diag.py`, with the window's UI Automation text as truth. All
+  recognizers read the same crops.
+- **On the NPU** (Ryzen 7 8700G): bit-exact to the XINT8 ONNX in every bucket, at 10-17 ms a line (30 ms for a
+  split long line).
+- **Weak spot:** busy terminal UIs. It reads a space as '-' next to box-drawing rules, and it inserts glyphs
+  that aren't in the dictionary (spinners, check marks).
+
+## Training data and fonts: read before use
+
+- **Text:** public domain only, plus generators: PG19 (public-domain books) and the SQLite source.
+- **Fonts:** the line images were rendered with Windows GDI and DirectWrite from the fonts installed on the
+  training PC, as whole lines of text, not single glyphs. No font file or glyph outline is in this model. By
+  family, of the 264 used:
+  - **about 93 are Windows' own fonts** (Segoe UI, Consolas, Cascadia, Calibri, Arial and others). They carry
+    most of the training lines: the generator draws most lines from a fixed list of these.
+  - **about 56 are openly licensed** (OFL, Apache-2.0, Ubuntu Font Licence and similar: Noto, DejaVu,
+    Liberation, Ubuntu Sans Mono and others).
+  - **about 115 came with other software**, mostly Microsoft Office: Monotype, ITC, URW, Font Bureau and
+    Bigelow & Holmes faces such as Gill Sans MT, Bodoni MT and Agency FB; also an ASUS and an Adobe font. Their
+    licences neither grant nor clearly forbid this use. They make up about 16% of the training lines, about
+    10 k lines per family.
+- **The publisher's reading:** the model contains no font data, so publishing it does not distribute any font.
+  This is not a settled legal question. Users who need certainty should take their own advice. A version
+  trained only on Windows-supplied and openly licensed fonts can be made if needed.
+
+## Licence
+
+AGPL-3.0-or-later, like ignite-scribe. `dict.txt` is from PaddleOCR (Apache-2.0); see `r03/LICENSE.md`.
+`manifest.json` lists every file with its sha256.

manifest.json

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+{
+  "repo": "scriberec",
+  "source": "https://github.com/jdominick05/ignite-scribe",
+  "files": {
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+      "sha256": "686724e414001ca3a9e559d6c5dcff82bb7ee2457cfede1c17f05dbc32539efa",
+      "bytes": 4023
+    },
+    "r03/LICENSE.md": {
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+      "bytes": 837
+    },
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+  }
+}

r03/LICENSE.md

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+# ScribeRec r03: licence and sources
+
+**This model:** AGPL-3.0-or-later (https://www.gnu.org/licenses/agpl-3.0.html), the licence of ignite-scribe.
+It was trained from scratch: no pretrained weights were used.
+
+**`dict.txt`:** the PP-OCRv5 English character dictionary from [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR),
+Apache License 2.0 (http://www.apache.org/licenses/LICENSE-2.0), Copyright (c) PaddlePaddle Authors, unchanged.
+
+**Training text:**
+- [PG19](https://github.com/google-deepmind/pg19): public-domain books from Project Gutenberg (dataset Apache-2.0);
+- the [SQLite](https://sqlite.org/copyright.html) source, which is public domain;
+- text generators in ignite-scribe and ignite-xdna (AGPL-3.0).
+
+**Fonts:** rendered on the training PC, not distributed. See the model card's "Training data and fonts" section.

r03/MODEL.md

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+# ScribeRec SMALL, run r03
+
+This is release-01 fine-tuned on detector-style terminal crops (stacked rows, neighbour bleed, edge glyphs)
+(2026-10-20, RX 7900 XTX). The architecture, the 438-class dictionary and the whole input/output contract are
+unchanged from release-01 (see its README), so it is a drop-in replacement. The training text is still public
+domain only (PG19 + SQLite source) plus our generators.
+
+## Files
+
+The layout is the same as release-01: `backbone_{320,640,960,1280}.onnx` plus `_xint8` versions (Quark XINT8,
+calibrated per bucket on 128 seed-7 lines); `backbone.onnx` and `backbone_xint8.onnx` (the 320 versions);
+`tail.onnx` (dynamic n and t); `scriberec_small.pt` (state_dict, SMALL); `dict.txt`, `results.json`,
+`train.log` and `log.jsonl` (r02b), plus `r02-train.log` and `r02-log.jsonl` (the first stage).
+
+## Results (`r03/last.pt`, the end of the schedule; not picked on the eval sets)
+
+| Backbone | seed-100 CER | seed-100 exact | wiki-heldout CER | wiki-heldout exact |
+|---|---|---|---|---|
+| float ONNX | 0.00145 | 97.4% | 0.00069 | 98.2% |
+| **XINT8 ONNX** | **0.00154** | **97.2%** | **0.00061** | **98.4%** |
+
+- **Synthetic terminal** (the r03 crop style, seed 101, PyTorch float): CER 0.00452, 98.6% exact. On the same
+  set, release-01 scores 0.0729 / 51.8%.
+- **Caveat:** the synthetic set only shows the model learned the generator. The acceptance test is ignite-scribe's
+  static-terminal `ocr_diag` gate: <= 1.5x PP-OCRv5, i.e. about 0.0070, where release-01 measures 0.015.
+
+## What changed from release-01
+
+- **Starting point:** a fine-tune of **release-01**. r02/r02b, whose ink-tight crops failed the live gate, are
+  not in its history.
+- **Terminal family, 30% of lines** (`data.term_block`):
+  - 3 rows stacked at the font's cell pitch with zero leading; each neighbour row is another terminal line, or
+    blank 25% of the time;
+  - the crop is U(0.8, 1.4) x pitch tall, centred on the target ink with N(0, 0.06 pitch) jitter, with 0-0.25
+    pitch of horizontal padding;
+  - left edge, 35% of lines: a prompt, box-drawing, spinner or cursor glyph that isn't in the dict, 2-10 px before
+    the text; 40% of these are cut glyphs;
+  - right edge, 15%: a cursor block or a cut glyph;
+  - labels are the target row only.
+  These come from ignite-scribe's live diagnostics: crops average 1.15 row pitches, and top-edge ink shows up in
+  91% of release-01's error lines.
+- **Other lines:** 30% are drawn with DirectWrite (ClearType, grayscale or aliased, at 96-192 DPI); no ink-tight
+  re-crops.
+- **Lookalikes:** the symbol exclusion from r02b applies (`data.HOMOGLYPHS`).
+
+## Recipe
+
+- AdamW lr 5e-4, 300 warmup steps, cosine to 2%;
+- 9,000 steps x 256 lines (2.3 M lines, 73 min);
+- mix term/dw/ink 0.3/0.3/0.0;
+- public-domain corpus.

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