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MinerU: An Open-Source Solution for Precise Document Content Extraction

Bin Wang
Chao Xu
Xiaomeng Zhao
Linke Ouyang
Fan Wu
Zhiyuan Zhao
Rui Xu
Kaiwen Liu
Yuan Qu
Fukai Shang
Bo Zhang
Liqun Wei
Zhihao Sui
Wei Li
Botian Shi
Yu Qiao
Dahua Lin
Conghui He
Abstract

Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, existing open-source solutions struggle to consistently deliver high-quality content extraction due to the diversity in document types and content. To address these challenges, we present MinerU, an open-source solution for high-precision document content extraction. MinerU leverages the sophisticated PDF-Extract-Kit models to extract content from diverse documents effectively and employs finely-tuned preprocessing and postprocessing rules to ensure the accuracy of the final results. Experimental results demonstrate that MinerU consistently achieves high performance across various document types, significantly enhancing the quality and consistency of content extraction. The MinerU open-source project is available at https://github.com/opendatalab/MinerU.

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