Fully convolutional network with dilated convolutions for handwritten text line segmentation

Fully convolutional network with dilated convolutions for handwritten text line segmentation We present a learning-based method for handwritten text line segmentation in document images. Our approach relies on a variant of deep fully convolutional networks (FCNs) with dilated convolutions. Dilated convolutions allow to never reduce the input resolution and produce a pixel-level labeling. The FCN is trained to identify X-height labeling as text line representation, which has many advantages for text recognition. We show that our approach outperforms the most popular variants of FCN, based on deconvolution or unpooling layers, on a public dataset. We also provide results investigating various settings, and we conclude with a comparison of our model with recent approaches defined as part of the cBAD ( https://scriptnet.iit.demokritos.gr/competitions/5/ ) international competition, leading us to a 91.3% F-measure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Document Analysis and Recognition (IJDAR) Springer Journals

Fully convolutional network with dilated convolutions for handwritten text line segmentation

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Image Processing and Computer Vision; Pattern Recognition
ISSN
1433-2833
eISSN
1433-2825
D.O.I.
10.1007/s10032-018-0304-3
Publisher site
See Article on Publisher Site

Abstract

We present a learning-based method for handwritten text line segmentation in document images. Our approach relies on a variant of deep fully convolutional networks (FCNs) with dilated convolutions. Dilated convolutions allow to never reduce the input resolution and produce a pixel-level labeling. The FCN is trained to identify X-height labeling as text line representation, which has many advantages for text recognition. We show that our approach outperforms the most popular variants of FCN, based on deconvolution or unpooling layers, on a public dataset. We also provide results investigating various settings, and we conclude with a comparison of our model with recent approaches defined as part of the cBAD ( https://scriptnet.iit.demokritos.gr/competitions/5/ ) international competition, leading us to a 91.3% F-measure.

Journal

International Journal of Document Analysis and Recognition (IJDAR)Springer Journals

Published: May 30, 2018

References

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