| library_name: Doc-UFCN | |
| license: mit | |
| tags: | |
| - Doc-UFCN | |
| - PyTorch | |
| - Object detection | |
| metrics: | |
| - IoU | |
| - F1 | |
| - [email protected] | |
| - [email protected] | |
| - AP@[.5,.95] | |
| # Hugin-Munin line detection | |
| 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/). | |
| ## Model description | |
| The model has been trained using the Doc-UFCN library on Hugin-Munin document images. | |
| It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio. | |
| The model predicts two classes: vertical and horizontal text lines. | |
| ## Evaluation results | |
| The model achieves the following results: | |
| | set | class | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] | | |
| | ----- | ---------- | ----- | ----- | ------- | -------- | ----------- | | |
| | train | vertical | 88.29 | 89.67 | 71.37 | 33.26 | 36.32 | | |
| | | horizontal | 69.81 | 81.35 | 91.73 | 36.62 | 45.67 | | |
| | val | vertical | 73.01 | 75.13 | 46.02 | 4.99 | 15.58 | | |
| | | horizontal | 61.65 | 75.69 | 87.98 | 11.18 | 31.55 | | |
| | test | vertical | 78.62 | 80.03 | 59.93 | 15.90 | 24.11 | | |
| | | horizontal | 63.59 | 76.49 | 95.93 | 24.18 | 41.45 | | |
| ## 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{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} | |
| } | |
| ``` | |