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---
library_name: Doc-UFCN
license: mit
tags:
- Doc-UFCN
- PyTorch
- Object detection
metrics:
- IoU
- F1
- AP@.5
- AP@.75
- AP@[.5,.95]
---
# Generic historical line detection
The generic historical line detection model predicts text lines from document images.
## Model description
The model has been trained using the Doc-UFCN library on 10 historical document datasets including these public datasets:
* [Bozen](https://zenodo.org/record/218236)
* [cBAD2017 (READ)](https://zenodo.org/record/1491441)
* [cBAD2019](https://zenodo.org/record/2567398)
* [DIVA-HisDB](https://diuf.unifr.ch/main/hisdoc/diva-hisdb.html)
* [Horae](https://github.com/oriflamms/HORAE/)
* [ScribbleLens](https://www.openslr.org/84/)
It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.
## Evaluation results
The model achieves the following results on the test sets:
| | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] |
| ----------------------- | ----- | ----- | ------- | -------- | ----------- |
| Bozen | 60.15 | 75.10 | 97.14 | 3.79 | 27.50 |
| cBAD2017 (READ) Complex | 46.79 | 60.35 | 56.01 | 3.40 | 16.26 |
| cBAD2017 (READ) Simple | 53.97 | 68.43 | 57.26 | 8.45 | 19.39 |
| cBAD2019 | 50.77 | 64.52 | 35.46 | 2.88 | 11.51 |
| DIVA-HisDB | 41.54 | 57.88 | 63.15 | 0.00 | 11.69 |
| Horae | 48.93 | 63.95 | 57.45 | 5.20 | 15.55 |
| ScribbleLens | 76.61 | 86.72 | 98.02 | 71.87 | 58.32 |
The model has been trained to reduce mergers in predictions (see the [paper](https://link.springer.com/article/10.1007/s10032-022-00395-7) for more details on training). Therefore, despite slightly low evaluation values, the model correctly detects lines on a wide variety of historical and modern manuscript documents.
## 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{boillet2022,
author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}},
booktitle = {{International Journal on Document Analysis and Recognition (IJDAR)}},
year = {2022},
month = Mar,
pages = {1433-2825},
doi = {10.1007/s10032-022-00395-7}
}
```
```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}
}
```
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