| --- |
| tags: |
| - optical-character-recognition |
| - image-to-text |
| - kashmiri |
| - perso-arabic |
| - pytorch |
| language: |
| - ks |
| library_name: pytorch |
| --- |
| |
| # Koshur OCR v2 |
|
|
| A compact Kashmiri Perso-Arabic **printed-line** OCR model from **Kash Labs AI**. |
|
|
| ## v2.1 results — prefix beam + train-only character LM |
|
|
| - **Beam deduplicated validation CER:** 21.35% (greedy: 22.62%) |
| - **Beam deduplicated exact match:** 26.20% (greedy: 25.54%) |
| - **Held-out 720-row CER:** 21.24% (greedy: 22.44%) |
| - **Absolute held-out CER improvement:** 1.19 percentage points |
| - **Validation rows:** 920 deduplicated / 1,000 full |
| - **Best epoch:** 18 |
| - **Training rows:** 100,000 synthetic sentence-line images |
| - **Beam evaluation:** https://wandb.ai/kash-ai-labs-kash-labs/koshur-pixel-ocr/runs/ctc-v2-prefix-beam-20260729T152530Z |
|
|
| The selected decoder uses width 10, top-12 token pruning, a trigram character LM trained only on the 100,000 training labels, LM weight 0.4, and token bonus 1.5. It was tuned on a fixed 200-row calibration subset and independently improved the remaining 720 rows. Across all 920 rows, deletions fell from 6,859 to 4,615. |
|
|
| ## Architecture |
|
|
| `64×2048 grayscale line → CNN (48→96→160→192) → 2-layer bidirectional GRU (256 per direction) → 155-way CTC` |
|
|
| The model has **4,475,931 trainable parameters**, a 154-character inventory, and no temporal feature dropout in the selected v2 control checkpoint. This is a custom PyTorch checkpoint, not a Transformers-format model. |
|
|
| ## Versioning |
|
|
| - `v1.0.0` — 10k temporal-dropout checkpoint, 28.13% deduplicated CER |
| - `v2.0.0` — 100k control checkpoint with greedy decoding, 22.62% deduplicated CER |
| - `v2.1.0` — same weights plus prefix-beam/trigram decoding, 21.35% deduplicated CER (current) |
|
|
| Use a Hub revision to pin a release. |
|
|
| ## Files |
|
|
| - `best-model.pt` — epoch-18 state dictionary, character inventory, architecture metadata, and validation CER |
| - `model.py` — architecture, preprocessing, CTC decoding, and `recognize_line()` |
| - `ctc_prefix_beam.py` — tested prefix-beam implementation |
| - `char-trigram-lm.pkl` — compact train-only character language model |
| - `beam-eval.json` — calibration, held-out, and full evaluation metrics |
| - `config.json` — release, training, preprocessing, metrics, and checkpoint checksum |
|
|
| ## Local CPU or Apple Silicon inference |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| ```python |
| from PIL import Image |
| from model import recognize_line |
| |
| text, diagnostics = recognize_line(Image.open("single-line.png")) |
| print(text) |
| print(diagnostics) |
| ``` |
|
|
| Prefix beam is the default. For the original fast decoder: |
|
|
| ```python |
| text, diagnostics = recognize_line(Image.open("single-line.png"), decoder="greedy") |
| ``` |
|
|
| The input should be one tightly cropped, upright printed line. It is resized to height 64 and left-aligned on a width-2048 canvas. Inference runs on CPU and does not require a GPU. |
|
|
| ## Limitations |
|
|
| - The reported score is an in-domain synthetic validation result, not real-world scan quality. |
| - Trained on synthetic printed lines, not handwriting or full pages. |
| - Camera photos should be rotated, deskewed, tightly cropped, and segmented into individual lines. |
| - Training targets exclude the established Kashmiri diacritic set; the model recognizes base text and does not restore those marks. |
| - Error rate remains substantial, with deletions the dominant residual error. |
| - Predictions require review before publication or archival use. |
|
|
