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This model is designed for Arabic Optical Character Recognition (OCR).

Model Details

Model Description

This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: Ahmed Wasfy, Omer Nacar, Abdelakreem Elkhateb, Mahmoud Reda, Omar Elshehy, Adel Ammar, Wadii Boulila
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: Vision-Language Model for OCR
  • Language(s) (NLP): Arabic
  • License: [More Information Needed]
  • Finetuned from model [optional]: Qwen2-VL-2B-Instruct

Model Sources [optional]

Uses

Direct Use

This model can be directly used for recognizing Arabic text in images.

Downstream Use [optional]

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Out-of-Scope Use

This model is specifically designed for Arabic text and might not perform well on other languages.

Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

Trained on specialized synthetic datasets.

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

@misc{QariOCR2025,
  title={QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation},
  author={Ahmed Wasfy, Omer Nacar, Abdelakreem Elkhateb, Mahmoud Reda, Omar Elshehy, Adel Ammar, Wadii Boulila},
  year={2025},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2506.02295},
  note={Accessed: 2025-03-03}
}

APA:

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Glossary [optional]

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