Datasets:

context212
/

context212-alhazen-ocr

license: cc-by-4.0
task_categories:
  - image-to-text
language:
  - ar
  - fr
  - en
tags:
  - ocr
  - arabic
  - handwriting
  - invoices
  - document-understanding
size_categories:
  - 10K<n<100K
pretty_name: Alhazen-OCR Data
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: eval
        path: data/eval-*

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:

Downloads each source from the Hub (with retry/backoff for rate limits)
Normalizes every source to the image + text schema, renaming columns as needed and casting to a common feature type
Samples each source to its target share of the mix (seed 42, reproducible)
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}
}