MinerU: An Open-Source Solution for Precise Document Content Extraction
Authors: name: Bin Wang; name: Chao Xu; name: Xiaomeng Zhao; name: Linke Ouyang; name: Fan Wu; name: Zhiyuan Zhao; name: Rui Xu; name: Kaiwen Liu; name: Yuan Qu; name: Fukai Shang; name: Bo Zhang; name: Liqun Wei; name: Zhihao Sui; name: Wei Li; name: Botian Shi; name: Yu Qiao; name: Dahua Lin; name: Conghui He
Published: 2024-09-27 15:35:15

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.