title: QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large
    Language Model Adaptation
summary: 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.
paper_authors._id: 683fa2ddf0564d1fb4b863c1
paper_authors.name: Ahmed Wasfy
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paper_authors.fullname: Ahmed Wasfy
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paper_authors.name: Omer Nacar
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paper_authors.fullname: Omartificial Intelligence Space
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paper_authors.user: Omartificial-Intelligence-Space
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paper_authors.name: Abdelakreem Elkhateb
paper_authors._id: 683fa2ddf0564d1fb4b863c4
paper_authors.name: Mahmoud Reda
paper_authors._id: 683fa2ddf0564d1fb4b863c5
paper_authors.name: Omar Elshehy
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paper_authors.fullname: Omar Elshehy
paper_authors.type: user
paper_authors.user: omarelshehy
paper_authors._id: 683fa2ddf0564d1fb4b863c6
paper_authors.name: Adel Ammar
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paper_authors.fullname: Adel Ammar
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paper_authors.user: ammaradel
paper_authors._id: 683fa2ddf0564d1fb4b863c7
paper_authors.name: Wadii Boulila
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/e08afba50d8aae8f7698ccba4c1e7b4a.svg
paper_authors.fullname: Wadii Boulila
paper_authors.type: user
paper_authors.user: wboulila
paper_title: QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large
    Language Model Adaptation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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