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International Journal on Document Analysis and Recognition (IJDAR)
Published in: International Journal on Document Analysis and Recognition (IJDAR) 3/2018

30-05-2018 | Special Issue Paper

Fully convolutional network with dilated convolutions for handwritten text line segmentation

Authors: Guillaume Renton, Yann Soullard, Clément Chatelain, Sébastien Adam, Christopher Kermorvant, Thierry Paquet

Published in: International Journal on Document Analysis and Recognition (IJDAR) | Issue 3/2018

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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.
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Footnotes
1
Please note that a preliminary work has been presented at the ICDAR-WML workshop [25].
 
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Metadata
Title
Fully convolutional network with dilated convolutions for handwritten text line segmentation
Authors
Guillaume Renton
Yann Soullard
Clément Chatelain
Sébastien Adam
Christopher Kermorvant
Thierry Paquet
Publication date
30-05-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal on Document Analysis and Recognition (IJDAR) / Issue 3/2018
Print ISSN: 1433-2833
Electronic ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-018-0304-3

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