PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
About
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
Cheng Cui, Ting Sun, Suyin Liang, Tingquan Gao, Zelun Zhang, Jiaxuan Liu, Xueqing Wang, Changda Zhou, Hongen Liu, Manhui Lin, Yue Zhang, Yubo Zhang, Yi Liu, Dianhai Yu, Yanjun Ma• 2026
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Optical Character Recognition | OCRBench | Score549 | 232 | |
| Document Parsing | OmniDocBench v1.5 | Overall Score94.5 | 195 | |
| Document Parsing | OmniDocBench 1.5 (test) | Text Edit Error0.035 | 111 | |
| Table Structure Recognition | PubTabNet | S-TEDS84.6 | 37 | |
| Document Parsing | OmniDocBench Real5 skewing variation | Overall Score91.66 | 32 | |
| Document Parsing | OmniDocBench Real5 warping | Overall Score91.25 | 32 | |
| Document Parsing | Real5-OmniDocBench (screen-photography) | Overall Score91.76 | 32 | |
| Document Recognition | OmniDocBench | Overall Score94.5 | 29 | |
| Document Parsing | OmniDocBench Real5 | Score92.16 | 26 | |
| Document Parsing | OmniDocBench Full v1.6 | Overall Accuracy94.87 | 21 |
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