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

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46 Pith papers citing it
14 external citations · Pith
Background 55% of classified citations
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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2026 42 2025 4

representative citing papers

FollowTable: A Benchmark for Instruction-Following Table Retrieval

cs.IR · 2026-05-01 · unverdicted · novelty 8.0

FollowTable is the first large-scale benchmark for instruction-following table retrieval, paired with an Instruction Responsiveness Score, showing that existing models fail to adapt to fine-grained constraints beyond topical similarity.

ParseBench: A Document Parsing Benchmark for AI Agents

cs.CV · 2026-04-09 · accept · novelty 7.0

ParseBench is a new benchmark for document parsing in AI agents that reveals fragmented performance across five semantic dimensions with LlamaParse Agentic scoring highest at 84.9%.

MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing

cs.AI · 2026-05-21 · unverdicted · novelty 6.0 · 2 refs

MPDocBench-Parse provides 433 annotated multi-page documents and an evaluation protocol covering text/table/formula extraction, merging, figure extraction, reading order, and heading hierarchy for realistic document parsing.

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Showing 46 of 46 citing papers.