Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915512731041792 |
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| author | Hennara, Khalil Hreden, Muhammad Hamed, Mohamed Motasim Bastati, Ahmad Aldallal, Zeina Chrouf, Sara AlModhayan, Safwan |
| author_facet | Hennara, Khalil Hreden, Muhammad Hamed, Mohamed Motasim Bastati, Ahmad Aldallal, Zeina Chrouf, Sara AlModhayan, Safwan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18174 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR Hennara, Khalil Hreden, Muhammad Hamed, Mohamed Motasim Bastati, Ahmad Aldallal, Zeina Chrouf, Sara AlModhayan, Safwan Computer Vision and Pattern Recognition Computation and Language 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. |
| title | Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2509.18174 |