Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910805616754688 |
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| author | Livathinos, Nikolaos Auer, Christoph Lysak, Maksym Nassar, Ahmed Dolfi, Michele Vagenas, Panos Ramis, Cesar Berrospi Omenetti, Matteo Dinkla, Kasper Kim, Yusik Gupta, Shubham de Lima, Rafael Teixeira Weber, Valery Morin, Lucas Meijer, Ingmar Kuropiatnyk, Viktor Staar, Peter W. J. |
| author_facet | Livathinos, Nikolaos Auer, Christoph Lysak, Maksym Nassar, Ahmed Dolfi, Michele Vagenas, Panos Ramis, Cesar Berrospi Omenetti, Matteo Dinkla, Kasper Kim, Yusik Gupta, Shubham de Lima, Rafael Teixeira Weber, Valery Morin, Lucas Meijer, Ingmar Kuropiatnyk, Viktor Staar, Peter W. J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion Livathinos, Nikolaos Auer, Christoph Lysak, Maksym Nassar, Ahmed Dolfi, Michele Vagenas, Panos Ramis, Cesar Berrospi Omenetti, Matteo Dinkla, Kasper Kim, Yusik Gupta, Shubham de Lima, Rafael Teixeira Weber, Valery Morin, Lucas Meijer, Ingmar Kuropiatnyk, Viktor Staar, Peter W. J. Computation and Language Computer Vision and Pattern Recognition Software Engineering 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. |
| title | Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion |
| topic | Computation and Language Computer Vision and Pattern Recognition Software Engineering |
| url | https://arxiv.org/abs/2501.17887 |