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--- |
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tags: |
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- ocr |
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- arabic |
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- document-understanding |
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- structure-preservation |
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- computer-vision |
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pretty_name: "Misraj-DocOCR" |
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license: apache-2.0 |
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--- |
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# Misraj-DocOCR: An Arabic Document OCR Benchmark📄 |
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**Dataset:** [Misraj/Misraj-DocOCR](https://huggingface.co/datasets/Misraj/Misraj-DocOCR) |
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**Domain:** Arabic Document OCR (text + structure) |
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**Size:** 400 expertly verified pages (real + synthetic) |
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**Use cases:** OCR, Document Understanding, Markdown/HTML structure preservation |
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**Status:** Public 🤝 |
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## ✨ Overview |
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**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**. |
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- **Diverse content:** books, reports, forms, scholarly pages, and complex layouts. |
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- **Expert-verified ground truth:** human-reviewed for text **and** structure. |
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- **Open & reproducible:** intended for fair comparisons and reliable benchmarking. |
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--- |
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## 📦 Data format |
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Each example typically includes: |
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- `uuid`: id of sample |
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- `image`: page image (PIL-compatible) |
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- `markdown`: target transcription with structure |
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### 🔌 Loading |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("Misraj/Misraj-DocOCR") |
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split = ds["train"] # or another available split |
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ex = split[0] |
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img = ex["image"] # PIL.Image |
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gt = ex.get("markdown") or ex.get("text") |
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print(gt[:400]) |
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# img.show() # uncomment in a local environment |
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``` |
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--- |
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## 🧪 Metrics |
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We report both **text** and **structure** metrics: |
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* **Text:** WER ↓, CER ↓, BLEU ↑, ChrF ↑ |
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* **Structure:** **TEDS ↑**, **MARS ↑** (Markdown/HTML structure fidelity) |
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--- |
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## 🏆 Leaderboard (Misraj-DocOCR) |
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Best values are **bold**, second-best are <u>underlined</u>. |
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| Model | WER ↓ | CER ↓ | BLEU ↑ | CHRF ↑ | TEDS ↑ | MARS ↑ | |
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| ----------------------------- | ---------: | ---------: | ----------: | ----------: | -------: | -----------: | |
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| **Baseer (ours)** | **0.25** | 0.53 | <u>76.18</u> | <u>87.77</u> | **66** | **76.885** | |
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| Gemini-2.5-pro | <u>0.37</u> | <u>0.31</u> | **77.92** | **89.55** | <u>52</u> | <u>70.775</u> | |
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| Azure AI Document Intelligence[^azure] | 0.44 | **0.27** | 62.04 | 82.49 | 42 | 62.245 | |
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| Dots.ocr | 0.50 | 0.40 | 58.16 | 78.41 | 40 | 59.205 | |
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| Nanonets | 0.71 | 0.55 | 42.22 | 67.89 | 37 | 52.445 | |
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| Qari | 0.76 | 0.64 | 38.59 | 64.50 | 21 | 42.750 | |
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| Qwen2.5-VL-32B | 0.76 | 0.59 | 37.62 | 62.64 | 41 | 51.820 | |
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| GPT-5 | 0.86 | 0.62 | 40.67 | 61.6 | 48 | 54.8 | |
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| Qwen2.5-VL-3B-Instruct | 0.87 | 0.71 | 25.39 | 53.42 | 27 | 40.210 | |
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| Qwen2.5-VL-7B | 0.92 | 0.77 | 31.57 | 54.70 | 27 | 40.850 | |
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| Gemma3-12B | 0.96 | 0.80 | 19.75 | 44.53 | 33 | 38.765 | |
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| Gemma3-4B | 1.01 | 0.85 | 9.57 | 31.39 | 28 | 29.695 | |
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| GPT-4o-mini | 1.36 | 1.10 | 22.63 | 47.04 | 26 | 36.52 | |
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| AIN | 1.23 | 1.11 | 1.25 | 2.24 | 21 | 11.620 | |
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| Aya-vision | 1.41 | 1.07 | 2.91 | 9.81 | 26 | 17.905 | |
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**Highlights:** |
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* **Baseer (ours)** leads on **WER**, **TEDS**, and **MARS** → strong text & structure fidelity. |
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* **Gemini-2.5-pro** tops **BLEU/ChrF**; **Azure AI Document Intelligence** attains lowest **CER**. |
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--- |
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## 📚 How to cite |
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If you use **Misraj-DocOCR**, please cite: |
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```bibtex |
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@misc{hennara2025baseervisionlanguagemodelarabic, |
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title={Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR}, |
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author={Khalil Hennara and Muhammad Hreden and Mohamed Motasim Hamed and Ahmad Bastati and Zeina Aldallal and Sara Chrouf and Safwan AlModhayan}, |
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year={2025}, |
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eprint={2509.18174}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2509.18174}, |
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} |
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``` |
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