Docling Technical Report

About

This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.

Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Nikolaos Livathinos, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Fabian Lindlbauer, Kasper Dinkla, Lokesh Mishra, Yusik Kim, Shubham Gupta, Rafael Teixeira de Lima, Valery Weber, Lucas Morin, Ingmar Meijer, Viktor Kuropiatnyk, Peter W. J. Staar · 2024

Related benchmarks

Task	Dataset	Result
Document Text Generation	OHR-Bench Generation	Text Score46.6	14
Textual RAG	OHR-Bench (Overall)	TXT Score0.43	14
Document Retrieval	OHR-Bench Retrieval	Accuracy (Text)73.3	14
Visual RAG	BizMMRAG	Score (TXT)66.7	5
Textual RAG	BizMMRAG Japanese (test)	TXT Score70	5
Textual RAG	Allganize Japanese (test)	TXT Score61.3	5
Visual RAG	OHR-Bench (test)	TXT Score73.2	5
Visual RAG	Allganize	TXT Score66	5