File size: 2,698 Bytes
e36f209 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | # Alef-OCR-Image2Html
An Arabic OCR model that transforms document images,including historical texts, scanned pages, and handwritten materials—into structured and semantic HTML.
### Key Features
- **Semantic HTML Output:** Generates structured HTML with semantic tags (section, header, main, footer, table, etc.)
- **Multi-format Support:** Handles various document types including historical manuscripts, newspaper articles, scientific papers, invoices, and more
- **Arabic-Optimized:** Fine-tuned specifically for Arabic text recognition and structure extraction
- **Zero-cost Training:** Developed using Kaggle's free tier computational resources
## Model Architecture
- **Base Model:** Qwen2.5-VL-Instruct
- **Fine-tuning Method:** QLoRA with 4-bit quantization
- **LoRA Configuration:** Rank 16 applied to all modules
- **Optimization:** Unsloth for memory efficiency and training speed
## Training Data
The model was trained on a custom dataset of **28K image-HTML pairs** consisting of:
- **46% Web-scraped content** (~13K samples): Arabic Wikipedia articles with cleaned semantic HTML
- **54% Synthetic data** (~15K samples): Generated documents mimicking ~13 real-world formats with diverse layouts and styles
For more details, see the [arabic-image2html dataset](https://huggingface.co/datasets/OussamaBenSlama/arabic-image2html).
## Training Procedure
Training was performed in two stages:
**Stage 1:**
- Data: 40% of training dataset
- Learning rate: 5e-5
- LR scheduler: Linear
**Stage 2:**
- Data: 30% of training dataset (different split)
- Learning rate: 1e-5
- LR scheduler: Cosine
## Performance
Evaluated by the NAMAA community on an anonymous benchmark dataset:
| Model | WER | CER | BLEU |
|-------|-----|-----|------|
| Alef-OCR-Image2Html | 0.92 | **0.72** | **0.19** |
| Qari-OCR-v0.3 (baseline) | **0.84** | 0.73 | 0.17 |
**Key Results:**
- Better Character Error Rate (CER): 0.72 vs 0.73
- Better BLEU Score: 0.19 vs 0.17
- Higher Word Error Rate (WER) due to limited diacritics handling in training data
## Related Resources
- **Dataset:** [arabic-image2html](https://huggingface.co/datasets/OussamaBenSlama/arabic-image2html)
- **Training and Inference Notebooks:** [Available in the repository](https://github.com/OussamaBenSlama/Alef-OCR-Image2Html)
## Citation
```bibtex
@misc{alef_ocr_image2html_2025,
title={Alef-OCR-Image2Html: Arabic OCR to Semantic HTML},
author={Oussama Ben Slama},
year={2025},
howpublished={Hugging Face Models},
url={https://huggingface.co/OussamaBenSlama/Alef-OCR-Image2Html}
}
```
## Acknowledgments
This work builds upon:
- The NAMAA community's state-of-the-art Qari-OCR model
## License
Apache2.0
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