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Reza2kn/surya-ocr-2-persian-handwriting-369k

sourceHugging Faceopenrailupdated 2mo agoView on Hugging Face

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Model Card

Surya OCR 2 — Persian Handwriting 369K

This is a full-parameter fine-tune of `datalab-to/surya-ocr-2` on all 369,000 pages of `Reza2kn/persian-handwriting-pages-369k`.

Training

—Epochs: 1
—Rows: 369,000
—Optimizer: AdamW
—Precision: BF16
—Learning rate: 1e-05
—Per-GPU batch: 8
—Global batch: 64
—GPUs: 8 x NVIDIA RTX PRO 6000 Blackwell Server Edition
—Update steps: 5,766
—Parameter strategy: full-parameter fine-tuning (not LoRA)
—Prompt: OCR this Persian document image. Return only the complete transcription in reading order.

Fixed 69-page Persian before/after diagnostic

Both rows use the same model family, BF16 inference, prompt, first 69 dataset pages, normalization, and word-error implementation. The exact ordered image/label set is fingerprinted as 09ee91df3d0a920a14233b9ad769fe29a5861384a2fe86b409c9979155682e19 in both summaries.

These 69 pages are part of the 369,000-page training corpus. This is therefore an in-training-set acquisition diagnostic, useful for measuring the requested before/after fit but not a held-out estimate of Persian generalization.

Model	WER	Word errors / reference words	Exact pages
Original Surya OCR 2	149.28%	18,797 / 12,592	0/69
This checkpoint	2.89%	364 / 12,592	46/69

WER can exceed 100% because insertions count as errors. The checkpoint changes WER by -146.39 percentage points (-98.06% relative) on this fixed diagnostic.

Held-out Persian retention

These checks were added after training to measure whether the full-parameter fine-tune preserved Persian OCR outside the composed 369K training pages. Both models used identical images, prompt, normalization, decoding, and BF16 inference. Raw predictions and summaries are under evaluation/.

Printed Persian: ParsynthOCR test split

The first 69 examples from the held-out test split of `mainkilora/parsynth-ocr-200k` were evaluated at dataset revision b8f6db1bb635a801f8f222144643d2c52db49b18. The exact ordered image/text set has fingerprint 677c9ff212f5a2e4bea545f235e829fcf1231373e5c23404f2f87b6045ca3753.

Model	WER	Word errors / reference words	Exact images
Original Surya OCR 2	23.87%	37 / 155	46/69
This checkpoint	168.39%	261 / 155	21/69

This checkpoint substantially degrades on this printed-Persian word-image sample and frequently emits extra text.

Real Persian handwriting: PHTD holdout

This check uses 65 real Persian handwritten word crops from the separately prepared PHTD real-holdout set. Its labeled manifest fingerprint is f225f23fbec246e1ff034693647ef6f57ed95899824b056993b99bcfcf7a92ab. Because every reference contains one word, exact recognition and CER are more informative than WER when a model emits multiple words.

Model	WER	CER	Exact words	Multiword outputs
Original Surya OCR 2	95.38%	54.01%	8/65	5/65
This checkpoint	116.92%	74.56%	18/65	16/65

Exact recognition more than doubles, but aggregate WER and CER worsen because the checkpoint produces more multiword hallucinations. This is not clean retention: it improves some handwritten words while becoming less reliable overall.

Narrow retention canary

This 69-item English handwritten-word crop test is a deliberately narrow forgetting canary, not a comprehensive general OCR benchmark. Surya sometimes emits long layout-style text for these tiny crops, so exact matches are more interpretable than its inflated WER.

Model	WER	Exact words
Original Surya OCR 2	800.00%	24/69
Step 500	832.20%	9/69
Step 2,000	988.14%	6/69
Step 3,000	805.08%	6/69
Final checkpoint	1266.10%	5/69

Raw predictions, references, summaries, and manifests are included under evaluation/ for auditing.

Limitations

The training data is synthetic composed Persian handwriting on photographed paper backgrounds. This checkpoint may trade some of the base model's broader OCR/layout behavior for specialization. Evaluate it on your own printed, handwritten, multilingual, and layout-heavy documents before deployment.