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PaddleOCR 3.0 Technical Report

About

This technical report introduces PaddleOCR 3.0, an Apache-licensed open-source toolkit for OCR and document parsing. To address the growing demand for document understanding in the era of large language models, PaddleOCR 3.0 presents three major solutions: (1) PP-OCRv5 for multilingual text recognition, (2) PP-StructureV3 for hierarchical document parsing, and (3) PP-ChatOCRv4 for key information extraction. Compared to mainstream vision-language models (VLMs), these models with fewer than 100 million parameters achieve competitive accuracy and efficiency, rivaling billion-parameter VLMs. In addition to offering a high-quality OCR model library, PaddleOCR 3.0 provides efficient tools for training, inference, and deployment, supports heterogeneous hardware acceleration, and enables developers to easily build intelligent document applications.

Cheng Cui, Ting Sun, Manhui Lin, Tingquan Gao, Yubo Zhang, Jiaxuan Liu, Xueqing Wang, Zelun Zhang, Changda Zhou, Hongen Liu, Yue Zhang, Wenyu Lv, Kui Huang, Yichao Zhang, Jing Zhang, Jun Zhang, Yi Liu, Dianhai Yu, Yanjun Ma• 2025

Related benchmarks

TaskDatasetResultRank
Document ParsingOmniDocBench v1.5
Overall Score86.73
126
Document ParsingOmniDocBench 1.5 (test)
Overall Score86.73
27
Document ParsingReal5-OmniDocBench scanning scenario 1.5 (test)
Overall Score84.68
19
Document ParsingReal5-OmniDocBench 5-distortion types (test)
Overall Accuracy64.45
19
Document ParsingReal5-OmniDocBench (screen-photography)
Overall Score66.89
19
Document ParsingOmniDocBench Real5 illumination
Overall Score0.7338
19
Document ParsingOmniDocBench Real5 warping
Overall Score59.34
19
Document ParsingOmniDocBench Real5 skewing variation
Overall Score37.98
19
Document RecognitionUniRec-Bench
Book Error0.1
12
Scientific document parsingUni-Parser Benchmark 1.0 (test)
Overall Accuracy (excl. Mol.)82.41
8
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