updated the README

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

README.md

@@ -1,3 +1,73 @@
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 license: cdla-permissive-2.0
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+
+# Docling Models
+
+This page contains models that power the PDF document converion package [dockling](https://github.com/DS4SD/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}
+}
+```