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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

TaskDatasetResultRank
Document ParsingOmniDocBench 1.5 (test)
Overall Score94.5
27
Document ParsingReal5-OmniDocBench (screen-photography)
Overall Score91.76
19
Document ParsingReal5-OmniDocBench 5-distortion types (test)
Overall Accuracy92.05
19
Document ParsingReal5-OmniDocBench scanning scenario 1.5 (test)
Overall Score93.43
19
Document ParsingOmniDocBench Real5 skewing variation
Overall Score91.66
19
Document ParsingOmniDocBench Real5 illumination
Overall Score0.9216
19
Document ParsingOmniDocBench Real5 warping
Overall Score91.25
19
Document ParsingOmniDocBench v1.5
Total Time (s)944.4
9
Seal Recognitionin-house-seal benchmark
NED0.138
3
Text SpottingIn-house OCR benchmark
Overall Score86.21
3
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