PaddleOCR 3.0 Technical Report

2025-07-07

AI Summary

In response to the growing demand for multilingual document understanding in the large-model era, this work proposes three lightweight document intelligence solutions: PP-OCRv5, PP-StructureV3, and PP-ChatOCRv4. Methodologically, we integrate text detection, recognition, layout analysis, and vision-language modeling into a unified, end-to-end trainable framework built on PaddlePaddle, supporting heterogeneous hardware acceleration. Our key contributions are: (1) achieving state-of-the-art accuracy on multilingual OCR, hierarchical document parsing, and key information extraction—comparable to billion-parameter vision-language models—despite operating at the hundred-megabyte parameter scale; (2) providing an efficient, production-ready toolchain for training, inference, and deployment. All models and tools are open-sourced as a high-quality OCR library, substantially lowering the barrier to deploying document intelligence across diverse domains including finance, government services, and education.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Multilingual text recognition for diverse language support
• Hierarchical document parsing for structured data extraction
• Key information extraction to enhance document understanding

Innovation

• PP-OCRv5 for multilingual text recognition
• PP-StructureV3 for hierarchical document parsing
• PP-ChatOCRv4 for key information extraction