Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
This toolkit addresses document conversion challenges for developers and researchers working with AI applications, though it appears to be an incremental integration of existing models into a new package.
The authors introduced Docling, an open-source toolkit for converting various document formats into a unified structured representation using specialized AI models for layout analysis and table recognition, which gained 10k GitHub stars in less than a month and became the top trending repository globally in November 2024.
We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation. 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. Docling is released as a Python package and can be used as a Python API or as a CLI tool. Docling's modular architecture and efficient document representation make it easy to implement extensions, new features, models, and customizations. Docling has been already integrated in other popular open-source frameworks (e.g., LangChain, LlamaIndex, spaCy), making it a natural fit for the processing of documents and the development of high-end applications. The open-source community has fully engaged in using, promoting, and developing for Docling, which gathered 10k stars on GitHub in less than a month and was reported as the No. 1 trending repository in GitHub worldwide in November 2024.