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arXiv · 2012.14163

Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks

Mélodie Boillet·Christopher Kermorvant·Thierry Paquet
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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on natural scene images, our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents.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.

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Mélodie Boillet, Christopher Kermorvant, Thierry Paquet. 2020-12-28. Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks. https://doi.org/10.1109/icpr48806.2021.9412447

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