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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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metrics: |
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- IoU |
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- F1 |
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- AP@.5 |
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- AP@.75 |
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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 page detection |
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The generic page detection model predicts single pages from document images. |
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## Model description |
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The model has been trained using the Doc-UFCN library on [Horae](https://github.com/oriflamms/HORAE/) and [READ-BAD](https://github.com/ctensmeyer/pagenet) datasets. |
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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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## Evaluation results |
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The model achieves the following results: |
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| | set | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] | |
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| ----- | -------- | ----: | ----: | ------: | -------: | ----------: | |
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| HOME | test | 93.92 | 95.84 | 98.98 | 98.98 | 97.61 | |
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| Horae | test | 96.68 | 98.31 | 99.76 | 98.49 | 98.08 | |
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| Horae | test-300 | 95.66 | 97.27 | 98.87 | 98.45 | 97.38 | |
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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{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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``` |
