pdf-parser-mineru

Tool List

1. pdf_to_markdown

Convert PDF documents to Markdown format, preserving document structure, formulas, tables, and images.

Description: Use MinerU to parse PDF documents and output in Markdown format, supporting OCR, formula recognition, table extraction, and other features.

Parameters: - file_path (string, required): Absolute path to the PDF file - output_dir (string, required): Absolute path to the output directory - backend (string, optional): Parsing backend, options: hybrid-auto-engine (default), pipeline, vlm-auto-engine - language (string, optional): OCR language code, such as en (English), ch (Chinese), ja (Japanese), etc., defaults to auto-detection - enable_formula (boolean, optional): Whether to enable formula recognition, defaults to true - enable_table (boolean, optional): Whether to enable table extraction, defaults to true - start_page (integer, optional): Start page number (starting from 0), defaults to 0 - end_page (integer, optional): End page number (starting from 0), defaults to -1 meaning parse all pages

Return Value:

{
  "success": true,
  "output_path": "/path/to/output",
  "markdown_content": "Converted Markdown content...",
  "images": ["List of image paths"],
  "tables": ["List of table information"],
  "formula_count": 10
}

Examples:

python .claude/skills/pdf-process/script/pdf_parser.py 
  '{"name": "pdf_to_markdown", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output"}}'

# Use specific backend
python .claude/skills/pdf-process/script/pdf_parser.py 
  '{"name": "pdf_to_markdown", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output", "backend": "pipeline"}}'

# Parse specific pages
python .claude/skills/pdf-process/script/pdf_parser.py 
  '{"name": "pdf_to_markdown", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output", "start_page": 0, "end_page": 5}}'

2. pdf_to_json

Convert PDF documents to JSON format, including detailed layout and structural information.

Description: Use MinerU to parse PDF documents and output in JSON format, containing structured information such as text blocks, images, tables, formulas, etc.

Return Value:

{
  "success": true,
  "output_path": "/path/to/output.json",
  "pages": [
    {
      "page_no": 0,
      "page_size": [595, 842],
      "blocks": [
        {
          "type": "text",
          "text": "Text content",
          "bbox": [x, y, x, y]
        }
      ],
      "images": [],
      "tables": [],
      "formulas": []
    }
  ],
  "metadata": {
    "total_pages": 10,
    "author": "Author",
    "title": "Title"
  }
}

Examples:

python .claude/skills/pdf-process/script/pdf_parser.py 
  '{"name": "pdf_to_json", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output"}}'

# Use specific backend and language
python .claude/skills/pdf-process/script/pdf_parser.py 
  '{"name": "pdf_to_json", "arguments": {"file_path": "/path/to/document.pdf", "output_dir": "/path/to/output", "backend": "hybrid-auto-engine", "language": "ch"}}'

Installation Instructions

1. Install MinerU

# Update pip and install uv
pip install --upgrade pip
pip install uv

# Install MinerU (including all features)
uv pip install -U "mineru[all]"

2. Verify Installation

# Check if MinerU is installed successfully
mineru --version

# Test basic functionality
mineru --help

3. System Requirements

Python Version: 3.10-3.13
Operating System: Linux / Windows / macOS 14.0+
Memory:
Using pipeline backend: minimum 16GB, recommended 32GB+
Using hybrid/vlm backend: minimum 16GB, recommended 32GB+
Disk Space: minimum 20GB (SSD recommended)
GPU (optional):
pipeline backend: supports CPU-only
hybrid/vlm backend: requires NVIDIA GPU (Volta architecture and above) or Apple Silicon

Use Cases

Academic Paper Parsing: Extract structured content such as formulas, tables, and images
Technical Document Conversion: Convert PDF documents to Markdown for version control and online publishing
OCR Processing: Process scanned PDFs and garbled PDFs
Multilingual Documents: Supports OCR recognition for 109 languages
Batch Processing: Batch convert multiple PDF documents

Backend Selection Recommendations

hybrid-auto-engine (default): Balanced accuracy and speed, suitable for most scenarios
pipeline: Suitable for CPU-only environments, best compatibility
vlm-auto-engine: Highest accuracy, requires GPU acceleration

Notes

File Paths: All paths must be absolute paths
Output Directory: Non-existent directories will be created automatically
Performance: Using GPU can significantly improve parsing speed
Page Numbers: Page numbers start counting from 0
Memory: Processing large documents may consume more memory

Troubleshooting

Common Issues

Installation Failure:
Ensure using Python 3.10-3.13
Windows only supports Python 3.10-3.12 (ray does not support 3.13)
Using uv pip install can resolve most dependency conflicts
Insufficient Memory:
Use pipeline backend
Limit parsing pages: start_page and end_page
Reduce virtual memory allocation
Slow Parsing Speed:
Enable GPU acceleration
Use hybrid-auto-engine backend
Disable unnecessary features (formulas, tables)
Low OCR Accuracy:
Specify the correct document language
Ensure the backend supports OCR (use pipeline or hybrid-*)

MinerU Official Documentation: https://opendatalab.github.io/MinerU/
MinerU GitHub: https://github.com/opendatalab/MinerU
Online Demo: https://mineru.net/