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README.md
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## Description
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The arabic-large-nougat OCR is an end-to-end structured Optical Character Recognition (OCR) system designed specifically for the Arabic language.
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## How to Get Started with the Model
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Or, use the code below to get started with the model locally.
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```python
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from PIL import Image
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import torch
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### Citation
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If you find this model useful, please
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```bibtex
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@misc{rashad2024arabicnougatfinetuningvisiontransformers,
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title={Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction},
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### Disclaimer
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The arabic-
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## Description
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<div align="center">
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<!-- **Affiliations:** -->
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[**Github**](https://github.com/MohamedAliRashad/arabic-nougat) 🤗 [**Hugging Face**](https://huggingface.co/collections/MohamedRashad/arabic-nougat-673a3f540bd92904c9b92a8e) 📝 [**Paper**](https://arxiv.org/abs/2411.17835) 🗂️ [**Data**](https://huggingface.co/datasets/MohamedRashad/arabic-img2md) 📽️ [**Demo**](https://huggingface.co/spaces/MohamedRashad/Arabic-Nougat)
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</div>
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The arabic-large-nougat OCR is an end-to-end structured Optical Character Recognition (OCR) system designed specifically for the Arabic language.
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This model was trained from scratch based on the new tokenizer [riotu-lab/Aranizer-PBE-86k](https://huggingface.co/riotu-lab/Aranizer-PBE-86k) with the base nougat architecture.
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The training happened using the [MohamedRashad/arabic-img2md](https://huggingface.co/datasets/MohamedRashad/arabic-img2md) dataset.
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## How to Get Started with the Model
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Or, use the code below to get started with the model locally.
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Don't forget to update transformers:
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`pip install -U transformers`
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```python
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from PIL import Image
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import torch
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### Citation
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If you find this model useful, please cite the corresponding research paper:
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```bibtex
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@misc{rashad2024arabicnougatfinetuningvisiontransformers,
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title={Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction},
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### Disclaimer
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The arabic-large-nougat OCR is a tool provided "as is," and the developers make no guarantees regarding its suitability for specific tasks. Users are encouraged to thoroughly evaluate the model's output for their particular use cases and requirements.
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