Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks
Abstract
In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line segmentation task and more generally the layout analysis problem as a pixel-wise classification task then our model outputs a pixel-labeling of the input images. We show that Doc-UFCN outperforms state-of-the-art methods on various datasets and also demonstrate that the pre-trained parts on natural scene images are not required to reach good results. In addition, we show that pre-training on multiple document datasets can improve the performances. We evaluate the models using various metrics to have a fair and complete comparison between the methods.
Cite
@article{arxiv.2012.14163,
title = {Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks},
author = {Mélodie Boillet and Christopher Kermorvant and Thierry Paquet},
journal= {arXiv preprint arXiv:2012.14163},
year = {2021}
}