readme: ---
  library_name: Doc-UFCN
  license: mit
  tags:
  - Doc-UFCN
  - PyTorch
  - Object detection
  metrics:
  - IoU
  - F1
  - AP@.5
  - AP@.75
  - 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}
  }
  ```
  