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title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}}, |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