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A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition

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Artificial Neural Networks and Machine Learning – ICANN 2012 (ICANN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7553))

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Abstract

This paper presents a recurrent neural networks applied to handwriting character recognition. The method Multi-dimensional Recurrent Neural Network is evaluated against classical techniques. To improve the model performance we propose the use of specialized Support Vector Machine combined whit the original Multi-dimensional Recurrent Neural Network in cases of confusion letters. The experiments were performed in the C-Cube database and compared with different classifiers. The hierarchical combination presented promising results.

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Author information

Authors and Affiliations

  1. Polytechnic School of Pernambuco, University of Pernambuco, 50.750-470, Recife, PE, Brazil

    Byron Leite Dantas Bezerra & Vinícius Braga de Andrade

  2. Center of Informatics, Federal University of Pernambuco, Cidade Universitária, 50.732-970, Recife, PE, Brazil

    Cleber Zanchettin

Authors
  1. Byron Leite Dantas Bezerra
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  2. Cleber Zanchettin
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  3. Vinícius Braga de Andrade
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Editor information

Editors and Affiliations

  1. Neuro Heuristic Research Group, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  2. Department of Informatics, Nicolaus Copernicus University, 87-100, Toruń, Poland

    Włodzisław Duch

  3. Center for Complex Systems Studies, Kalamazoo College, 49006, Kalamazoo, MI, USA

    Péter Érdi

  4. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, 16146, Genoa, Italy

    Francesco Masulli

  5. Institut für Neuroinformatik, Universität Ulm, 89069, Ulm, Germany

    Günther Palm

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© 2012 Springer-Verlag Berlin Heidelberg

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Bezerra, B.L.D., Zanchettin, C., de Andrade, V.B. (2012). A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_31

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  • DOI: https://doi.org/10.1007/978-3-642-33266-1_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33265-4

  • Online ISBN: 978-3-642-33266-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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