Description
# Document Parsing # PDF Extraction # OCR Recognition # Open Source Project # AI Tools
A high-precision document parsing engine built specifically for LLM · RAG · Agent scenarios.
MinerU is an open-source project focused on the extraction of complex document content, suitable for handling various materials such as papers, reports, scanned documents, and tabular data. It can automatically recognize text-based PDFs, layered PDFs, and scanned PDFs and choose the appropriate method for parsing. It supports the extraction of elements such as titles, body text, images, tables, and formulas, and can finally export to formats like Markdown, significantly reducing the time spent on manual document organization.
Complex Document Parsing: Supports handling complex content such as academic papers, technical documents, scanned files, and tabular documents.
PDF Type Recognition: Can automatically recognize text-based PDFs, layered PDFs, and scanned PDFs, and perform targeted processing.
Multi-Element Extraction: Supports the extraction of titles, body text, images, tables, and formulas from structured document content.
Table Recognition: Can extract content and restore structure from complex tables, garbled tables, and defective tables.
Formula Parsing: Suitable for handling papers and research materials that contain complex mathematical formulas.
Scanned Document OCR: Supports recognition of scanned documents, with strong robustness against blurriness, watermarks, and other issues.
Markdown Export: Parsed content can be exported to Markdown and other formats with one click, facilitating secondary editing and knowledge organization.
Open Source and Deployable: The project is open source, suitable for developers, local deployment, and enterprise document processing scenarios.
A high-precision document parsing engine built specifically for LLM · RAG · Agent scenarios.
MinerU is an open-source project focused on the extraction of complex document content, suitable for handling various materials such as papers, reports, scanned documents, and tabular data. It can automatically recognize text-based PDFs, layered PDFs, and scanned PDFs and choose the appropriate method for parsing. It supports the extraction of elements such as titles, body text, images, tables, and formulas, and can finally export to formats like Markdown, significantly reducing the time spent on manual document organization.
Software Features
Complex Document Parsing: Supports handling complex content such as academic papers, technical documents, scanned files, and tabular documents.
PDF Type Recognition: Can automatically recognize text-based PDFs, layered PDFs, and scanned PDFs, and perform targeted processing.
Multi-Element Extraction: Supports the extraction of titles, body text, images, tables, and formulas from structured document content.
Table Recognition: Can extract content and restore structure from complex tables, garbled tables, and defective tables.
Formula Parsing: Suitable for handling papers and research materials that contain complex mathematical formulas.
Scanned Document OCR: Supports recognition of scanned documents, with strong robustness against blurriness, watermarks, and other issues.
Markdown Export: Parsed content can be exported to Markdown and other formats with one click, facilitating secondary editing and knowledge organization.
Open Source and Deployable: The project is open source, suitable for developers, local deployment, and enterprise document processing scenarios.
