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Parsr

Stale
GitHub JavaScript Apache-2.0

Description

Transforms PDF, documents and images into enriched structured data with table recognition, reading order restoration, and Markdown output.

Key Features

  • Multi-format document parsing: supports PDF, DOCX, images, and EML files with minimal footprint
  • Structured output in JSON, Markdown, CSV/Pandas DataFrame, and TXT formats
  • Document structure detection: headings, tables, lists, table of contents, page numbers, headers/footers, and links
  • Table recognition and extraction with reading order restoration for accurate content preservation
  • Docker-based deployment with GUI for document submission and result visualization
  • Python client and Jupyter Notebook integration for programmatic document processing

Use Cases

💡 Automated data entry and document analysis automation for insurance and financial documents
💡 Converting scanned PDFs and images into structured, machine-readable data for downstream processing
💡 Archival and digitization of legacy documents with proper reading order and structure preservation
💡 RAG pipeline preprocessing: extracting clean text and structured data from documents for AI ingestion

Strengths & Limitations

✅ Strengths

  • • High community interest (6.2k stars)
  • • Permissive open-source license (Apache-2.0)

Tags

javascript rag tools data-processing automation

Categories

📚 RAG Tools

Quick Start

docker pull axarev/parsr
docker run -p 3001:3001 axarev/parsr

# API available at http://localhost:3001
# Install Python client:
pip install parsr-client

# Or use the GUI:
docker run -t -p 8080:80 axarev/parsr-ui-localhost:latest
Visit GitHub

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