Misraj-DocOCR: An Arabic Document OCR Benchmark📄

Dataset: Misraj/Misraj-DocOCR Domain: Arabic Document OCR (text + structure)
Size: 400 expertly verified pages (real + synthetic)
Use cases: OCR, Document Understanding, Markdown/HTML structure preservation
Status: Public 🤝

✨ Overview

Misraj-DocOCR is a curated, expert-verified benchmark for Arabic document OCR with an emphasis on structure preservation (Markdown/HTML tables, lists, footnotes, math, watermarks, multi-column, marginalia, etc.). Each page includes high-quality ground truth designed to evaluate both text fidelity and layout/structure fidelity.

• Diverse content: books, reports, forms, scholarly pages, and complex layouts.
• Expert-verified ground truth: human-reviewed for text and structure.
• Open & reproducible: intended for fair comparisons and reliable benchmarking.

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📦 Data format

Each example typically includes:

• uuid: id of sample
• image: page image (PIL-compatible)
• markdown: target transcription with structure

🔌 Loading

from datasets import load_dataset

ds = load_dataset("Misraj/Misraj-DocOCR")
split = ds["train"]  # or another available split

ex = split[0]
img = ex["image"]  # PIL.Image
gt  = ex.get("markdown") or ex.get("text")
print(gt[:400])
# img.show()  # uncomment in a local environment

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🧪 Metrics

We report both text and structure metrics:

• Text: WER ↓, CER ↓, BLEU ↑, ChrF ↑
• Structure: TEDS ↑, MARS ↑ (Markdown/HTML structure fidelity)

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🏆 Leaderboard (Misraj-DocOCR)

Best values are bold, second-best are underlined.

Model	WER ↓	CER ↓	BLEU ↑	CHRF ↑	TEDS ↑	MARS ↑
Baseer (ours)	0.25	0.53	76.18	87.77	66	76.885
Gemini-2.5-pro	0.37	0.31	77.92	89.55	52	70.775
Azure AI Document Intelligence[^azure]	0.44	0.27	62.04	82.49	42	62.245
Dots.ocr	0.50	0.40	58.16	78.41	40	59.205
Nanonets	0.71	0.55	42.22	67.89	37	52.445
Qari	0.76	0.64	38.59	64.50	21	42.750
Qwen2.5-VL-32B	0.76	0.59	37.62	62.64	41	51.820
GPT-5	0.86	0.62	40.67	61.6	48	54.8
Qwen2.5-VL-3B-Instruct	0.87	0.71	25.39	53.42	27	40.210
Qwen2.5-VL-7B	0.92	0.77	31.57	54.70	27	40.850
Gemma3-12B	0.96	0.80	19.75	44.53	33	38.765
Gemma3-4B	1.01	0.85	9.57	31.39	28	29.695
GPT-4o-mini	1.36	1.10	22.63	47.04	26	36.52
AIN	1.23	1.11	1.25	2.24	21	11.620
Aya-vision	1.41	1.07	2.91	9.81	26	17.905

Highlights:

• Baseer (ours) leads on WER, TEDS, and MARS → strong text & structure fidelity.
• Gemini-2.5-pro tops BLEU/ChrF; Azure AI Document Intelligence attains lowest CER.

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📚 How to cite

If you use Misraj-DocOCR, please cite:

@misc{hennara2025baseervisionlanguagemodelarabic,
      title={Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR}, 
      author={Khalil Hennara and Muhammad Hreden and Mohamed Motasim Hamed and Ahmad Bastati and Zeina Aldallal and Sara Chrouf and Safwan AlModhayan},
      year={2025},
      eprint={2509.18174},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.18174}, 
}