PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917101876281344 |
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| author | Cui, Cheng Sun, Ting Liang, Suyin Gao, Tingquan Zhang, Zelun Liu, Jiaxuan Wang, Xueqing Zhou, Changda Liu, Hongen Lin, Manhui Zhang, Yue Zhang, Yubo Zheng, Handong Zhang, Jing Zhang, Jun Liu, Yi Yu, Dianhai Ma, Yanjun |
| author_facet | Cui, Cheng Sun, Ting Liang, Suyin Gao, Tingquan Zhang, Zelun Liu, Jiaxuan Wang, Xueqing Zhou, Changda Liu, Hongen Lin, Manhui Zhang, Yue Zhang, Yubo Zheng, Handong Zhang, Jing Zhang, Jun Liu, Yi Yu, Dianhai Ma, Yanjun |
| contents | In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. Code is available at https://github.com/PaddlePaddle/PaddleOCR . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model Cui, Cheng Sun, Ting Liang, Suyin Gao, Tingquan Zhang, Zelun Liu, Jiaxuan Wang, Xueqing Zhou, Changda Liu, Hongen Lin, Manhui Zhang, Yue Zhang, Yubo Zheng, Handong Zhang, Jing Zhang, Jun Liu, Yi Yu, Dianhai Ma, Yanjun Computer Vision and Pattern Recognition In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. Code is available at https://github.com/PaddlePaddle/PaddleOCR . |
| title | PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.14528 |