Datasets:
metadata
license: apache-2.0
task_categories:
- text-to-image
language:
- ar
tags:
- arabic
- Qari
- OCR
- ArabicOCR
- BookStyle
- Markdown
pretty_name: Qari-OCR
size_categories:
- 10K<n<100K
QARI Markdown Mixed Dataset
📋 Dataset Summary
The QARI v0.3 Markdown Mixed Dataset is a specialized synthetic dataset designed for training Arabic OCR models with a focus on complex document layouts and HTML structure understanding. This dataset is part of the QARI-OCR project, which achieves state-of-the-art performance in Arabic text recognition.
This dataset contains 37,000 synthetically generated Arabic document images (29.6k train, 3.7k validation, 3.7k test) with corresponding ground truth text in HTML/Markdown format, featuring:
- 🔤 Full diacritical marks (tashkeel) support
- 📝 Mixed font sizes within documents (headers, body text, annotations)
- 🎨 12 distinct Arabic fonts ranging from common Naskh to ornate calligraphic styles
- 📄 Realistic document layouts with structural HTML tags
- 🖼️ Multiple text sources including Basma2423 and YoussefAnwar Arabic news
🎯 Intended Use
This dataset is specifically designed for:
- Training OCR models that need to understand document structure
- Fine-tuning vision-language models for Arabic text recognition
- Developing systems that preserve formatting and layout information
- Research in Arabic document analysis and understanding
📊 Dataset Statistics
| Metric | Value |
|---|---|
| Total Images | 37,000 |
| Train Set | 29,600 (80%) |
| Validation Set | 3,700 (10%) |
| Test Set | 3,700 (10%) |
| Text Sources | oddadmix/Basma2423-Text-with-Diacritics-Correction + YoussefAnwar/Arabic-news |
| Font Variety | 12 Arabic fonts |
| Font Size Range | 14px - 100px |
| Diacritics Support | ✅ Full tashkeel |
| HTML Structure | ✅ Preserved |
| Layout Complexity | ✅ High (mixed sizes, headers) |
🔧 Data Generation Pipeline
| Stage | Process | Details |
|---|---|---|
| 1. Text Collection | Source gathering | Basma2423 (with diacritics) + YoussefAnwar Arabic news |
| 2. HTML Templating | Layout generation | Mixed font sizes, structural elements |
| 3. Rendering | WeasyPrint → PDF → Image | High-quality document rendering |
| 4. Degradation | Synthetic noise | Clean / Moderate / Heavy variants |
📈 Model Performance
When used to train QARI v0.3, this dataset enables:
| Metric | Score |
|---|---|
| Character Error Rate (CER) | 0.300 |
| Word Error Rate (WER) | 0.485 |
| BLEU Score | 0.545 |
| Training Time | 11 hours |
| CO₂ Emissions | 1.88 kg eq. |
Key Advantages:
- 📐 Superior layout understanding compared to plain text models
- 🏷️ HTML tag preservation for structured document conversion
- ⚡ Resource efficient - 5x less training time than larger datasets
- 🎯 Specialized performance for document structure tasks
Citation
@article{wasfy2025qari,
title={QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation},
author={Wasfy, Ahmed and Nacar, Omer and Elkhateb, Abdelakreem and Reda, Mahmoud and Elshehy, Omar and Ammar, Adel and Boulila, Wadii},
journal={arXiv preprint arXiv:2506.02295},
year={2025}
}