context212/context212-alhazen-ocr

Alhazen-OCR Data

alhazen-ocr is the training dataset behind context212/alhazen-ocr, an Arabic-first OCR vision-language model. It combines license-clean Arabic OCR sources — synthetic documents, institutional invoices, and handwritten text — into a single normalized image + text format, with a held-out eval split for CER/WER benchmarking.

Quick links:

• 🤗 Model: context212/alhazen-ocr
• 🛠️ Code (data pipeline, training, eval): github.com/context212/atlas-ocr
• 📊 External benchmark: ahmedheakl/arocrbench_khatt

What's in the dataset

Each row is one document image with its full transcription:

• image: the document image (page, crop, or line, depending on source)
• text: the transcription in natural reading order
• source: the upstream dataset the row came from

Split	Rows
train	52,726
eval	1,000

Source composition

The mix targets 60% synthetic documents, 20% invoices, 20% handwriting. Where a source has fewer rows than its target share, all available rows are used.

Source	Content	Rows used	License
loay/arabic-ocr-synthetic-scans-faker-300k	Synthetic Arabic document scans	45,000	CC-BY-4.0
KhalfounMehdi/arabic-latin-invoices-synthetic	Invoices (ar/en/fr)	4,014	CC-BY-4.0
johnlockejrr/KHATT_v1.0_dataset	Handwritten Arabic (KFUPM)	4,672	MIT
sherif1313/Historical-Arabic-Handwritten-OCR	Historical handwritten pages	40	Apache-2.0

Deliberately excluded on license grounds (non-commercial): aamijar/muharaf-public (CC-BY-NC-SA), freococo/* (CC-BY-NC-ND). Every row in this dataset comes from a commercially usable source.

Build pipeline

scripts/build_dataset.py in the repo above:

1. Downloads each source from the Hub (with retry/backoff for rate limits)
2. Normalizes every source to the image + text schema, renaming columns as needed and casting to a common feature type
3. Samples each source to its target share of the mix (seed 42, reproducible)
4. Shuffles, splits off 1,000 rows for eval, and pushes both splits

Rebuild with:

uv sync
HF_TOKEN=hf_... uv run python scripts/build_dataset.py

Intended use

• Training / fine-tuning Arabic OCR vision-language models
• Evaluating OCR robustness across printed, invoice, and handwritten Arabic
• The eval split is the held-out set used in the model card of context212/alhazen-ocr — do not train on it

Not intended for:

• Languages other than Arabic/French/English (coverage is Arabic-first)
• High-stakes transcription without further validation

Limitations

• Roughly 85% of rows are synthetic documents; real scanned and handwritten Arabic is a small minority, so models trained on this mix alone may underperform on real handwriting (this shows up in the model's external benchmark scores — see the model card)
• The historical handwriting source contributed only 40 usable image/text pairs at build time
• Texts are not re-verified against the images; upstream transcription errors carry through

Licensing / Terms of Use

All sources are permissively licensed for commercial use (CC-BY-4.0, MIT, Apache-2.0). The dataset as a whole is released under CC-BY-4.0. If you use it, credit the upstream sources listed above.

Citation

@misc{alhazen_ocr_2026,
  title  = {Alhazen-OCR: an open Arabic-first OCR vision-language model},
  author = {Context212},
  year   = {2026},
  url    = {https://huggingface.co/context212/alhazen-ocr}
}