Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR
Authors: name: Khalil Hennara, name: Muhammad Hreden, name: Mohamed Motasim Hamed, name: Ahmad Bastati, name: Zeina Aldallal, name: Sara Chrouf, name: Safwan AlModhayan
Organization: Misraj Ai

Arabic document OCR remains a challenging task due to the language's cursive
script, diverse fonts, diacritics, and right-to-left orientation. While modern
Multimodal Large Language Models (MLLMs) have advanced document understanding
for high-resource languages, their performance on Arabic remains limited. In
this work, we introduce Baseer, a vision-language model fine- tuned
specifically for Arabic document OCR. Leveraging a large-scale dataset
combining synthetic and real-world documents, Baseer is trained using a
decoder-only fine-tuning strategy to adapt a pre-trained MLLM while preserving
general visual features. We also present Misraj-DocOCR, a high-quality,
expert-verified benchmark designed for rigorous evaluation of Arabic OCR
systems. Our experiments show that Baseer significantly outperforms existing
open-source and commercial solutions, achieving a WER of 0.25 and establishing
a new state-of-the-art in the domain of Arabic document OCR. Our results
highlight the benefits of domain-specific adaptation of general-purpose MLLMs
and establish a strong baseline for high-accuracy OCR on morphologically rich
languages like Arabic.