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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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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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--- |
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# Hugin-Munin line detection |
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The Hugin-Munin line detection model predicts text lines from Hugin-Munin document images. This model was developed during the [HUGIN-MUNIN project](https://hugin-munin-project.github.io/). |
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## Model description |
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The model has been trained using the Doc-UFCN library on Hugin-Munin document images. |
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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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The model predicts two classes: vertical and horizontal text lines. |
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## Evaluation results |
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The model achieves the following results: |
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| set | class | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] | |
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| ----- | ---------- | ----- | ----- | ------- | -------- | ----------- | |
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| train | vertical | 88.29 | 89.67 | 71.37 | 33.26 | 36.32 | |
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| | horizontal | 69.81 | 81.35 | 91.73 | 36.62 | 45.67 | |
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| val | vertical | 73.01 | 75.13 | 46.02 | 4.99 | 15.58 | |
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| | horizontal | 61.65 | 75.69 | 87.98 | 11.18 | 31.55 | |
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| test | vertical | 78.62 | 80.03 | 59.93 | 15.90 | 24.11 | |
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| | horizontal | 63.59 | 76.49 | 95.93 | 24.18 | 41.45 | |
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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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``` |
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