docling-models
| Property | Value |
|---|---|
| License | CDLA-Permissive-2.0 |
| Paper | arxiv:2408.09869 |
| Downloads | 42,003 |
| Framework | Transformers |
What is docling-models?
docling-models is a comprehensive suite of AI models designed for advanced document analysis and PDF processing. It consists of two main components: a layout detection model based on RT-DETR architecture and TableFormer for table structure recognition. The layout model achieves state-of-the-art performance in detecting 11 different document components including captions, footnotes, formulas, and tables.
Implementation Details
The model suite implements two specialized architectures: RT-DETR for layout detection and TableFormer for table structure understanding. The layout detection component can identify 11 different document elements with impressive accuracy, often approaching human-level performance. TableFormer achieves 93.6% accuracy across all table types, significantly outperforming traditional solutions like Tabula (67.9%) and Camelot (73.0%).
- Layout detection for 11 document components with performance comparable to human evaluation
- State-of-the-art table structure recognition with 95.4% accuracy on simple tables and 90.1% on complex tables
- Integration with the docling Python package for seamless PDF processing
Core Capabilities
- Advanced layout analysis for document components including text, tables, formulas, and headers
- High-precision table structure identification and extraction
- Support for both simple and complex document layouts
- Integration capabilities with PDF processing workflows
Frequently Asked Questions
Q: What makes this model unique?
The model combines cutting-edge layout detection with superior table structure recognition, achieving performance levels that approach or exceed human evaluation in many categories. Its comprehensive coverage of document elements and state-of-the-art performance in table structure recognition make it particularly valuable for document processing applications.
Q: What are the recommended use cases?
The model is ideal for automated document processing workflows, academic paper analysis, technical document conversion, and any application requiring precise extraction of structured content from PDFs. It's particularly strong in handling complex documents with mixed content types including tables, formulas, and various text elements.
