

# 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The model has been trained using the Doc-UFCN library on 10 historical document datasets including these public datasets: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    title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}},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}
}
```

{"license": "mit", "library_name": "Doc-UFCN", "tags": ["Doc-UFCN", "PyTorch", "Object detection"], "metrics": ["IoU", "F1", "AP@.5", "AP@.75", "AP@[.5,.95]"]}
Doc-UFCN PyTorch Object detection license:mit region:us
TAGS
#Doc-UFCN #PyTorch #Object detection #license-mit #region-us 

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
* cBAD2017 (READ)
* cBAD2019
* DIVA-HisDB
* Horae
* ScribbleLens


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:



The model has been trained to reduce mergers in predictions (see the paper 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 (URL to use this model.


Cite us!
========



TAGS
#Doc-UFCN #PyTorch #Object detection #license-mit #region-us 

TAGS
#Doc-UFCN #PyTorch #Object detection #license-mit #region-us 
