QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large
  Language Model Adaptation
Authors: name: Ahmed Wasfy, name: Omer Nacar, name: Abdelakreem Elkhateb, name: Mahmoud Reda, name: Omar Elshehy, name: Adel Ammar, name: Wadii Boulila

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.