KashlabsAI/Koshur-OCR

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

pip install -r requirements.txt

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:

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.