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Fully convolutional network with dilated convolutions for handwritten text line segmentation

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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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Notes

  1. Please note that a preliminary work has been presented at the ICDAR-WML workshop [25].

  2. https://scriptnet.iit.demokritos.gr/competitions/5/.

  3. https://scriptnet.iit.demokritos.gr/competitions/8/.

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Authors and Affiliations

  1. Normandie Univ, UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, 76000, Rouen, France

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

  2. TEKLIA SAS, Paris, France

    Christopher Kermorvant

Authors
  1. Guillaume Renton
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  2. Yann Soullard
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  3. Clément Chatelain
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  4. Sébastien Adam
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  5. Christopher Kermorvant
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  6. Thierry Paquet
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Corresponding author

Correspondence to Guillaume Renton.

Additional information

This work has been supported by the French National grant ANR 16-LCV2-0004-01 Labcom INKS. This work is founded by the French region Normandy and the European Union. Europe acts in Normandy with the European Regional Development Fund (ERDF).

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Renton, G., Soullard, Y., Chatelain, C. et al. Fully convolutional network with dilated convolutions for handwritten text line segmentation. IJDAR 21, 177–186 (2018). https://doi.org/10.1007/s10032-018-0304-3

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  • DOI: https://doi.org/10.1007/s10032-018-0304-3

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