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 | |
|---|---|---|---|---|
| Document Parsing | OmniDocBench 1.5 (test) | Overall Score94.5 | 27 | |
| Document Parsing | Real5-OmniDocBench (screen-photography) | Overall Score91.76 | 19 | |
| Document Parsing | Real5-OmniDocBench 5-distortion types (test) | Overall Accuracy92.05 | 19 | |
| Document Parsing | Real5-OmniDocBench scanning scenario 1.5 (test) | Overall Score93.43 | 19 | |
| Document Parsing | OmniDocBench Real5 skewing variation | Overall Score91.66 | 19 | |
| Document Parsing | OmniDocBench Real5 illumination | Overall Score0.9216 | 19 | |
| Document Parsing | OmniDocBench Real5 warping | Overall Score91.25 | 19 | |
| Document Parsing | OmniDocBench v1.5 | Total Time (s)944.4 | 9 | |
| Seal Recognition | in-house-seal benchmark | NED0.138 | 3 | |
| Text Spotting | In-house OCR benchmark | Overall Score86.21 | 3 |
Showing 10 of 10 rows