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Insights on the Use of Convolutional Neural Networks for Document Image Binarization

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Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9095))

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Abstract

Convolutional Neural Networks have systematically shown good performance in Computer Vision and in Handwritten Text Recognition tasks. This paper proposes the use of these models for document image binarization. The main idea is to classify each pixel of the image into foreground and background from a sliding window centered at the pixel to be classified. An experimental analysis on the effect of sensitive parameters and some working topologies are proposed using two different corpora, of very different properties: DIBCO and Santgall.

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Author information

Authors and Affiliations

  1. Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain

    J. Pastor-Pellicer, S. España-Boquera & Maria Jose Castro-Bleda

  2. Departamento de Ciencias Físicas, Matemáticas y de la Computación, Universidad CEU Cardenal Herrera, Alfara del Patriarca (Valencia), Valencia, Spain

    F. Zamora-Martínez

  3. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    M. Zeshan Afzal

Authors
  1. J. Pastor-Pellicer
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  2. S. España-Boquera
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  3. F. Zamora-Martínez
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  4. M. Zeshan Afzal
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  5. Maria Jose Castro-Bleda
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Corresponding author

Correspondence to J. Pastor-Pellicer .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. Department of Electronics Technology, University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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Pastor-Pellicer, J., España-Boquera, S., Zamora-Martínez, F., Afzal, M.Z., Castro-Bleda, M. (2015). Insights on the Use of Convolutional Neural Networks for Document Image Binarization. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-19222-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19221-5

  • Online ISBN: 978-3-319-19222-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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