QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large
  Language Model Adaptation
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.
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Ahmed Wasfy
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2025-06-04T01:36:46.007Z
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Ahmed Wasfy
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Omer Nacar
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2025-06-05T07:28:27.770Z
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Omartificial Intelligence Space
user
Omartificial-Intelligence-Space
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Abdelakreem Elkhateb
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Mahmoud Reda
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2025-07-21T08:30:53.459Z
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Mahmoud Reda
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Mahmoud3899
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Omar Elshehy
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2025-06-05T07:37:06.068Z
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Omar Elshehy
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omarelshehy
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Adel Ammar
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2025-06-05T07:37:12.263Z
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Adel Ammar
user
ammaradel
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Wadii Boulila
admin_assigned
2025-06-05T07:37:18.295Z
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Wadii Boulila
user
wboulila
QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large
  Language Model Adaptation
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.
Qari-OCR, a series of fine-tuned vision-language models, achieves state-of-the-art performance in Arabic OCR through iterative optimization on specialized datasets, handling diacritics, fonts, layouts, and low-resolution images.
vision-language models Qwen2-VL-2B-Instruct iterative fine-tuning synthetic datasets Word Error Rate Character Error Rate BLEU score tashkeel document layouts handwritten text structural document understanding