Announcement
MinerU: High-Quality PDF to Markdown & JSON Converter
An open-source, high-quality tool to extract data from PDFs, converting them into structured Markdown and JSON formats. Streamline your document processing workflows.
Project Introduction
Summary
MinerU is an advanced open-source project designed for robust and high-fidelity data extraction and document conversion from PDF files. It provides powerful capabilities to transform PDF content into flexible Markdown and structured JSON formats.
Problem Solved
Manually extracting data or converting complex documents from PDF is time-consuming, error-prone, and inefficient. MinerU automates this process, providing reliable structured outputs.
Core Features
High-Quality PDF to Markdown
Precisely convert complex PDF layouts into readable Markdown, preserving structure and formatting.
Structured Data Extraction to JSON
Extract structured data embedded within PDFs and output it as clean, parseable JSON.
All-in-One Data Extraction Solution
A comprehensive tool handling both document structure conversion and data extraction in one place.
Tech Stack
使用场景
MinerU can be applied in various scenarios requiring automated extraction and conversion of content from PDF documents.
Financial Document Processing
Details
Automate the extraction of financial data, tables, and text from quarterly reports or invoices into a structured JSON format for database import or analysis.
User Value
Saves significant manual data entry time and improves data accuracy from financial reports.
Academic & Content Conversion
Details
Convert academic papers, e-books, or articles from PDF into Markdown format for easier reading, annotation, or publication on blogs and websites.
User Value
Facilitates the repurposing and sharing of information from academic sources or e-books.
Legal Document Data Extraction
Details
Extract specific clauses, names, dates, or figures from legal documents or contracts into JSON for searchable databases or compliance checks.
User Value
Enables efficient searching and analysis across large volumes of legal texts.
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