MinerU
Turn complex papers and reports into structured content, including text, tables, and formulas.
CURATED / DOCUMENT INTELLIGENCE
From scanned pages to structured data and shared meaning.
Find the right building blocks for your next workflow.
A CONSIDERED STARTING POINT
Selected for specific workflows from public documentation. Not a hands-on ranking. Read the evidence standard.
One profile per tool. Multiple task categories where they help.
Turn complex papers and reports into structured content, including text, tables, and formulas.
Convert PDF and Office files into a consistent document model for retrieval and data workflows.
Recognize text in scanned pages, with document pipelines for layout, tables, and structured output.
Extract PDF tables into data frames and machine-readable tables for analysis.
Extract text, form fields, and table cells from document images through a managed API.
Encode document page images as multi-vectors and retrieve visual pages with text queries.
Connect document parsing, chunking, and retrieval to build answers with source references.
Convert Office files, PDFs, and other formats into Markdown for text processing and indexing.
Predict document types and logical page groups within a mixed document package.
Decode page images into task-specific fields or answers without a separate OCR stage.
Use prebuilt or custom models to extract document fields and their source regions.
Recognize text in page images and export plain text or word-level OCR information.
Parse complex documents and extract schema-defined fields with source-grounded results.
Build document-derived databases with DBMosaic or investigate long visual documents with DocMosaic.
Define a custom schema and extract structured fields with confidence information and source citations.
Combine complex-layout parsing with schema-based extraction for forms, invoices, and financial documents.
Build a semantic catalog from enterprise metadata and give agents consistent business definitions and query context.
Expose a versioned ontology layer to data agents and evolve it using execution evidence and paired evaluations.
Edit OWL ontologies and inspect model consistency when designing concepts and relationships for extracted data.
Govern enterprise concepts, relationships, and rules as shared context for agents and applications.
Map structured sources to RDF and query document-derived records alongside other enterprise data.
Ask questions across papers and a research library, inspect cited passages, and explore connections through research maps.
Extract document fields and tables into spreadsheets, validate records, and automate downstream document workflows.