| license |
| cdla-permissive-2.0 |
Docling Models
This page contains models that power the PDF document converion package docling.
Layout Model
The layout model will take an image from a poge and apply RT-DETR model in order to find different layout components. It currently detects the labels: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. As a reference (from the DocLayNet-paper), this is the performance of standard object detection methods on the DocLayNet dataset compared to human evaluation,
|
human |
MRCNN |
MRCNN |
FRCNN |
YOLO |
|
human |
R50 |
R101 |
R101 |
v5x6 |
| Caption |
84-89 |
68.4 |
71.5 |
70.1 |
77.7 |
| Footnote |
83-91 |
70.9 |
71.8 |
73.7 |
77.2 |
| Formula |
83-85 |
60.1 |
63.4 |
63.5 |
66.2 |
| List-item |
87-88 |
81.2 |
80.8 |
81.0 |
86.2 |
| Page-footer |
93-94 |
61.6 |
59.3 |
58.9 |
61.1 |
| Page-header |
85-89 |
71.9 |
70.0 |
72.0 |
67.9 |
| Picture |
69-71 |
71.7 |
72.7 |
72.0 |
77.1 |
| Section-header |
83-84 |
67.6 |
69.3 |
68.4 |
74.6 |
| Table |
77-81 |
82.2 |
82.9 |
82.2 |
86.3 |
| Text |
84-86 |
84.6 |
85.8 |
85.4 |
88.1 |
| Title |
60-72 |
76.7 |
80.4 |
79.9 |
82.7 |
| All |
82-83 |
72.4 |
73.5 |
73.4 |
76.8 |
TableFormer
The tableformer model will identify the structure of the table, starting from an image of a table. It uses the predicted table regions of the layout model to identify the tables. Tableformer has SOTA table structure identification,
| Model (TEDS) |
Simple table |
Complex table |
All tables |
| Tabula |
78.0 |
57.8 |
67.9 |
| Traprange |
60.8 |
49.9 |
55.4 |
| Camelot |
80.0 |
66.0 |
73.0 |
| Acrobat Pro |
68.9 |
61.8 |
65.3 |
| EDD |
91.2 |
85.4 |
88.3 |
| TableFormer |
95.4 |
90.1 |
93.6 |
References
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {{Docling Technical Report}},
url={https://arxiv.org/abs/2408.09869},
eprint={2408.09869},
doi = "10.48550/arXiv.2408.09869",
version = {1.0.0},
year = {2024}
}
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
}
@InProceedings{TableFormer2022,
author = {Nassar, Ahmed and Livathinos, Nikolaos and Lysak, Maksym and Staar, Peter},
title = {TableFormer: Table Structure Understanding With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {4614-4623},
doi = {https://doi.org/10.1109/CVPR52688.2022.00457}
}
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