The inherent complexities of Arabic script; its cursive nature, diacritical marks (tashkeel), and varied typography, pose persistent challenges for Optical Character Recognition (OCR). We present Qari-OCR, a series of vision-language models derived from Qwen2-VL-2B-Instruct, progressively optimized for Arabic through iterative fine-tuning on specialized synthetic datasets. Our leading model, QARI v0.2, establishes a new open-source state-of-the-art with a Word Error Rate (WER) of 0.160, Character Error Rate (CER) of 0.061, and BLEU score of 0.737 on diacritically-rich texts. Qari-OCR demonstrates superior handling of tashkeel, diverse fonts, and document layouts, alongside impressive performance on low-resolution images. Further explorations (QARI v0.3) showcase strong potential for structural document understanding and handwritten text. This work delivers a marked improvement in Arabic OCR accuracy and efficiency, with all models and datasets released to foster further research.
View on arXiv@article{wasfy2025_2506.02295,
title={ QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation },
author={ Ahmed Wasfy and Omer Nacar and Abdelakreem Elkhateb and Mahmoud Reda and Omar Elshehy and Adel Ammar and Wadii Boulila },
journal={arXiv preprint arXiv:2506.02295},
year={ 2025 }
}