Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR
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.
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Khalil Hennara
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2025-09-24T13:52:41.543Z
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Khalil Hennara
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Hennara
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Muhammad Hreden
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2025-09-24T13:52:34.689Z
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Muhammad Hreden
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Mohamed Motasim Hamed
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2025-09-24T13:52:31.754Z
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Mohamed Motasim Hamed
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Moatasem444
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Ahmad Bastati
admin_assigned
2025-09-24T15:25:38.733Z
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Ahmad Bastati
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Bastati
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Zeina Aldallal
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2025-09-24T13:52:37.383Z
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Zeina Aldallal
user
ZeinaD
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Sara Chrouf
68d38bec0e215259d193b38e
Safwan AlModhayan
Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR
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.
Baseer, a vision-language model fine-tuned for Arabic document OCR, achieves state-of-the-art performance using a decoder-only strategy and a large-scale dataset, outperforming existing solutions with a WER of 0.25.
Multimodal Large Language Models vision-language model decoder-only fine-tuning Misraj-DocOCR WER Arabic document OCR