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Towards Unsupervised Learning for Arabic Handwritten Recognition Using Deep Architectures

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

In the pattern recognition field and especially in the Handwriting recognition one, the Deep learning is becoming the new trend in Artificial Intelligence with the sheer size of raw data available nowadays. In this paper, we highlights how Deep Learning techniques can be effectively applied for recognizing Arabic handwritten script, our field of interest, and this by investigating two deep architectures: Deep Belief Network (DBN) and Convolutional Neural Networks (CNN). The two proposed architectures take the raw data as input and proceed with a greedy layer-wise unsupervised learning algorithm. The experimental study has proved promising results which are comparable or even superior to the standard classifiers with an efficiency of DBN over CNN architecture.

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Author information

Authors and Affiliations

  1. National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia

    Mohamed Elleuch

  2. Advanced Technologies for Medicine and Signals (ATMS), University of Sfax, Sfax, Tunisia

    Mohamed Elleuch & Monji Kherallah

  3. The Higher Institute of Management of Gabes, University of Gabes, Gabès, Tunisia

    Najiba Tagougui

Authors
  1. Mohamed Elleuch
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  2. Najiba Tagougui
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  3. Monji Kherallah
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Corresponding author

Correspondence to Mohamed Elleuch .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Elleuch, M., Tagougui, N., Kherallah, M. (2015). Towards Unsupervised Learning for Arabic Handwritten Recognition Using Deep Architectures. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_40

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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