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PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing

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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

TaskDatasetResultRank
Optical Character RecognitionOCRBench
Score549
232
Document ParsingOmniDocBench v1.5
Overall Score94.5
195
Document ParsingOmniDocBench 1.5 (test)
Text Edit Error0.035
111
Table Structure RecognitionPubTabNet
S-TEDS84.6
37
Document ParsingOmniDocBench Real5 skewing variation
Overall Score91.66
32
Document ParsingOmniDocBench Real5 warping
Overall Score91.25
32
Document ParsingReal5-OmniDocBench (screen-photography)
Overall Score91.76
32
Document RecognitionOmniDocBench
Overall Score94.5
29
Document ParsingOmniDocBench Real5
Score92.16
26
Document ParsingOmniDocBench Full v1.6
Overall Accuracy94.87
21
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