Update README.md

README.md

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----
-license: mit
----
+---
+library_name: Doc-UFCN
+license: mit
+tags:
+- Doc-UFCN
+- PyTorch
+- object-detection
+- dla
+- historical
+- handwritten
+- Samaritan
+metrics:
+- IoU
+- F1
+- AP@.5
+- AP@.75
+- AP@[.5,.95]
+pipeline_tag: image-segmentation
+---
+
+
+# Doc-UFCN - Samaritan manuscripts line detection
+
+The Samaritan manuscripts line detection model predicts text lines from document images.
+
+## Model description
+
+The model has been trained using the Doc-UFCN library on 10 Samaritan datasets:
+
+It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.
+
+The model has been trained to reduce mergers in predictions (see the [paper](https://link.springer.com/article/10.1007/s10032-022-00395-7) for more details on training). Therefore, despite slightly low evaluation values, the model correctly detects lines on a wide variety of historical and modern manuscript documents.
+
+## How to use?
+
+Please refer to the Doc-UFCN library page (https://pypi.org/project/doc-ufcn/) to use this model.
+
+## Cite us!
+
+```bibtex
+@inproceedings{boillet2022,
+    author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
+    title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}},
+    booktitle = {{International Journal on Document Analysis and Recognition (IJDAR)}},
+    year = {2022},
+    month = Mar,
+    pages = {1433-2825},
+    doi = {10.1007/s10032-022-00395-7}
+}
+```
+
+```bibtex
+@inproceedings{boillet2020,
+    author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
+    title = {{Multiple Document Datasets Pre-training Improves Text Line Detection With
+              Deep Neural Networks}},
+    booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
+    year = {2021},
+    month = Jan,
+    pages = {2134-2141},
+    doi = {10.1109/ICPR48806.2021.9412447}
+}
+```