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A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition

  • Byron Leite Dantas Bezerra
  • Cleber Zanchettin
  • Vinícius Braga de Andrade
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

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.

Keywords

Support Vector Machine Recognition Rate Recurrent Neural Network Character Recognition Handwriting Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Byron Leite Dantas Bezerra
    • 1
  • Cleber Zanchettin
    • 2
  • Vinícius Braga de Andrade
    • 1
  1. 1.Polytechnic School of PernambucoUniversity of PernambucoRecifeBrazil
  2. 2.Center of InformaticsFederal University of PernambucoRecifeBrazil

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