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--- |
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license: apache-2.0 |
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task_categories: |
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- text-to-image |
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language: |
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- ar |
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tags: |
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- arabic |
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- Qari |
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- OCR |
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- ArabicOCR |
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- BookStyle |
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- Markdown |
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pretty_name: Qari-OCR |
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size_categories: |
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- 10K<n<100K |
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--- |
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# QARI Markdown Mixed Dataset |
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<div align="center"> |
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</div> |
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<div align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/628f7a71dd993507cfcbe587/Txz_HjVy6NsdmcghXqVH_.png" alt="QARI Logo" width="400"> |
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</div> |
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## 📋 Dataset Summary |
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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. |
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This dataset is part of the QARI-OCR project, which achieves state-of-the-art performance in Arabic text recognition. |
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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: |
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- 🔤 **Full diacritical marks (tashkeel)** support |
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- 📝 **Mixed font sizes** within documents (headers, body text, annotations) |
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- 🎨 **12 distinct Arabic fonts** ranging from common Naskh to ornate calligraphic styles |
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- 📄 **Realistic document layouts** with structural HTML tags |
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- 🖼️ **Multiple text sources** including Basma2423 and YoussefAnwar Arabic news |
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## 🎯 Intended Use |
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This dataset is specifically designed for: |
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- Training OCR models that need to understand document structure |
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- Fine-tuning vision-language models for Arabic text recognition |
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- Developing systems that preserve formatting and layout information |
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- Research in Arabic document analysis and understanding |
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## 📊 Dataset Statistics |
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| Metric | Value | |
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|--------|-------| |
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| **Total Images** | 37,000 | |
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| **Train Set** | 29,600 (80%) | |
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| **Validation Set** | 3,700 (10%) | |
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| **Test Set** | 3,700 (10%) | |
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| **Text Sources** | oddadmix/Basma2423-Text-with-Diacritics-Correction + YoussefAnwar/Arabic-news | |
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| **Font Variety** | 12 Arabic fonts | |
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| **Font Size Range** | 14px - 100px | |
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| **Diacritics Support** | ✅ Full tashkeel | |
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| **HTML Structure** | ✅ Preserved | |
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| **Layout Complexity** | ✅ High (mixed sizes, headers) | |
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## 🔧 Data Generation Pipeline |
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<div align="center"> |
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| Stage | Process | Details | |
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|-------|---------|---------| |
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| **1. Text Collection** | Source gathering | Basma2423 (with diacritics) + YoussefAnwar Arabic news | |
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| **2. HTML Templating** | Layout generation | Mixed font sizes, structural elements | |
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| **3. Rendering** | WeasyPrint → PDF → Image | High-quality document rendering | |
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| **4. Degradation** | Synthetic noise | Clean / Moderate / Heavy variants | |
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</div> |
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## 📈 Model Performance |
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When used to train QARI v0.3, this dataset enables: |
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| Metric | Score | |
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|--------|-------| |
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| **Character Error Rate (CER)** | 0.300 | |
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| **Word Error Rate (WER)** | 0.485 | |
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| **BLEU Score** | 0.545 | |
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| **Training Time** | 11 hours | |
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| **CO₂ Emissions** | 1.88 kg eq. | |
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### Key Advantages: |
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- 📐 **Superior layout understanding** compared to plain text models |
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- 🏷️ **HTML tag preservation** for structured document conversion |
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- ⚡ **Resource efficient** - 5x less training time than larger datasets |
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- 🎯 **Specialized performance** for document structure tasks |
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## Citation |
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```markdown |
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@article{wasfy2025qari, |
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title={QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation}, |
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author={Wasfy, Ahmed and Nacar, Omer and Elkhateb, Abdelakreem and Reda, Mahmoud and Elshehy, Omar and Ammar, Adel and Boulila, Wadii}, |
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journal={arXiv preprint arXiv:2506.02295}, |
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year={2025} |
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} |
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``` |
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