PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
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
697c2ae8a67238fac88cc135
Cheng Cui
claimed_verified
2026-02-02T16:59:40.473Z
65a5231a087d8a2e9cc2414b
https://cdn-avatars.huggingface.co/v1/production/uploads/65a5231a087d8a2e9cc2414b/wj0l5R5LmBUG-E8XTMdBM.jpeg
cuicheng
ChengCui
user
ChengCui
697c2ae8a67238fac88cc136
Ting Sun
claimed_verified
2026-02-02T16:59:37.597Z
63d7b8ee07cd1aa3c49a2026
https://cdn-avatars.huggingface.co/v1/production/uploads/63d7b8ee07cd1aa3c49a2026/VdFETZ7zQKwkQhhahzW3i.jpeg
Ting Sun
sunflowerting78
user
sunflowerting78
697c2ae8a67238fac88cc137
Suyin Liang
697c2ae8a67238fac88cc138
Tingquan Gao
697c2ae8a67238fac88cc139
Zelun Zhang
697c2ae8a67238fac88cc13a
Jiaxuan Liu
697c2ae8a67238fac88cc13b
Xueqing Wang
697c2ae8a67238fac88cc13c
Changda Zhou
claimed_verified
2026-02-12T13:58:04.248Z
68c23142fc7c68f3c1be9788
https://cdn-avatars.huggingface.co/v1/production/uploads/68c23142fc7c68f3c1be9788/jDAZ0p5tDC9M1MOvhHY0J.png
chagndazhou
vvvic0415
user
vvvic0415
697c2ae8a67238fac88cc13d
Hongen Liu
697c2ae8a67238fac88cc13e
Manhui Lin
697c2ae8a67238fac88cc13f
Yue Zhang
697c2ae8a67238fac88cc140
Yubo Zhang
697c2ae8a67238fac88cc141
Yi Liu
claimed_verified
2026-02-27T16:46:03.211Z
6501403fea5699b59abacf15
https://cdn-avatars.huggingface.co/v1/production/uploads/6501403fea5699b59abacf15/Sh9ouC68HWHsF1Jh_0CGi.jpeg
Yi Liu
michaelowenliu
user
michaelowenliu
697c2ae8a67238fac88cc142
Dianhai Yu
697c2ae8a67238fac88cc143
Yanjun Ma
PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
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
A compact vision-language model achieves state-of-the-art accuracy on document understanding tasks while maintaining efficiency through specialized benchmarking and extended functionality.
Vision-Language Model OmniDocBench Real5-OmniDocBench seal recognition text spotting