Docling
Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.
About this tool
Overview
Docling is an open-source framework for parsing documents into structured data, developed by IBM. It excels at complex table extraction and provides self-hosting capabilities for privacy-sensitive applications.
Features
- High Accuracy: 97.9% accuracy in complex table extraction
- Excellent Text Fidelity: Preserves document structure and formatting
- Self-Hostable: Run entirely on-premises without API charges
- Multiple Formats: Handles PDFs, spreadsheets, scanned images
- Complex Layouts: Handles multi-column and nested structures
- Multimodal: Processes both text and images
- Open Source: Free to use and modify
Performance
Comprehensive evaluation reveals Docling as the superior framework for extracting structured data from sustainability reports. Achieves 100% accuracy on simple tables and 97.9% on complex structures. Processing time is 17+ seconds for complex documents but provides better accuracy than alternatives.
Use Cases
- Enterprise RAG pipelines requiring privacy
- Complex document processing (financial reports, research papers)
- Applications with sensitive data
- On-premises AI deployments
- Organizations wanting to avoid per-page API charges
Comparison
- vs LlamaParse: Slower but more accurate, self-hostable
- vs Unstructured: Better on complex tables, slower processing
Integration
Works with LangChain, LlamaIndex, and can be integrated with IBM watsonx.data and Granite models.
Pricing
Free and open-source. No per-page charges.
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