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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.00311 |
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| _version_ | 1866908475171274752 |
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| author | Zhong, Yufeng Zeng, Zhixiong Chen, Lei Yang, Longrong Zheng, Liming Huang, Jing Yang, Siqi Ma, Lin |
| author_facet | Zhong, Yufeng Zeng, Zhixiong Chen, Lei Yang, Longrong Zheng, Liming Huang, Jing Yang, Siqi Ma, Lin |
| contents | Optical Character Recognition (OCR) for mathematical formula is essential for the intelligent analysis of scientific literature. However, both task-specific and general vision-language models often struggle to handle the structural diversity, complexity, and real-world variability inherent in mathematical content. In this work, we present DocTron-Formula, a unified framework built upon general vision-language models, thereby eliminating the need for specialized architectures. Furthermore, we introduce CSFormula, a large-scale and challenging dataset that encompasses multidisciplinary and structurally complex formulas at the line, paragraph, and page levels. Through straightforward supervised fine-tuning, our approach achieves state-of-the-art performance across a variety of styles, scientific domains, and complex layouts. Experimental results demonstrate that our method not only surpasses specialized models in terms of accuracy and robustness, but also establishes a new paradigm for the automated understanding of complex scientific documents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DocTron-Formula: Generalized Formula Recognition in Complex and Structured Scenarios Zhong, Yufeng Zeng, Zhixiong Chen, Lei Yang, Longrong Zheng, Liming Huang, Jing Yang, Siqi Ma, Lin Computer Vision and Pattern Recognition Optical Character Recognition (OCR) for mathematical formula is essential for the intelligent analysis of scientific literature. However, both task-specific and general vision-language models often struggle to handle the structural diversity, complexity, and real-world variability inherent in mathematical content. In this work, we present DocTron-Formula, a unified framework built upon general vision-language models, thereby eliminating the need for specialized architectures. Furthermore, we introduce CSFormula, a large-scale and challenging dataset that encompasses multidisciplinary and structurally complex formulas at the line, paragraph, and page levels. Through straightforward supervised fine-tuning, our approach achieves state-of-the-art performance across a variety of styles, scientific domains, and complex layouts. Experimental results demonstrate that our method not only surpasses specialized models in terms of accuracy and robustness, but also establishes a new paradigm for the automated understanding of complex scientific documents. |
| title | DocTron-Formula: Generalized Formula Recognition in Complex and Structured Scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.00311 |