metadata

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.

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!

@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}
}