docling-project/docling-models
HuggingFaceDocling Models
This page contains models that power the PDF document converion package docling.
Layout Model
The layout model will take an image from a page 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}
}
```bibtex
@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} }