PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918426002325504 |
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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 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 Liu, Yi Yu, Dianhai Ma, Yanjun |
| contents | We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-photography, and illumination, we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model's capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency. Code: https://github.com/PaddlePaddle/PaddleOCR |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21957 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing Cui, Cheng Sun, Ting Liang, Suyin Gao, Tingquan Zhang, Zelun Liu, Jiaxuan Wang, Xueqing Zhou, Changda Liu, Hongen Lin, Manhui Zhang, Yue Zhang, Yubo Liu, Yi Yu, Dianhai Ma, Yanjun Computer Vision and Pattern Recognition We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-photography, and illumination, we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model's capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency. Code: https://github.com/PaddlePaddle/PaddleOCR |
| title | PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.21957 |