Docling Technical Report
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929620357480448 |
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| author | Auer, Christoph Lysak, Maksym Nassar, Ahmed Dolfi, Michele Livathinos, Nikolaos Vagenas, Panos Ramis, Cesar Berrospi Omenetti, Matteo Lindlbauer, Fabian Dinkla, Kasper Mishra, Lokesh Kim, Yusik Gupta, Shubham de Lima, Rafael Teixeira Weber, Valery Morin, Lucas Meijer, Ingmar Kuropiatnyk, Viktor Staar, Peter W. J. |
| author_facet | Auer, Christoph Lysak, Maksym Nassar, Ahmed Dolfi, Michele Livathinos, Nikolaos Vagenas, Panos Ramis, Cesar Berrospi Omenetti, Matteo Lindlbauer, Fabian Dinkla, Kasper Mishra, Lokesh Kim, Yusik Gupta, Shubham de Lima, Rafael Teixeira Weber, Valery Morin, Lucas Meijer, Ingmar Kuropiatnyk, Viktor Staar, Peter W. J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Docling Technical Report Auer, Christoph Lysak, Maksym Nassar, Ahmed Dolfi, Michele Livathinos, Nikolaos Vagenas, Panos Ramis, Cesar Berrospi Omenetti, Matteo Lindlbauer, Fabian Dinkla, Kasper Mishra, Lokesh 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 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. |
| title | Docling Technical Report |
| topic | Computation and Language Computer Vision and Pattern Recognition Software Engineering |
| url | https://arxiv.org/abs/2408.09869 |