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Convolutional Feature Learning and CNN Based HMM for Arabic Handwriting Recognition

  • Mustapha AmrouchEmail author
  • Mouhcine Rabi
  • Youssef Es-Saady
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

In this paper, we present a model CNN based HMM for Arabic handwriting word recognition. The HMM have proved a powerful to model the dynamics of handwriting. Meanwhile, the CNN have achieved impressive performance in many computer vision tasks, including handwritten characters recognition. In this model, the trainable classifier of CNN is replacing by the HMM classifier. CNN works as a generic feature extractor and HMM performs as a recognizer. The suggested system outperforms a basic HMM based on handcrafted features. Experiments have been conducted on the well-known IFN/ENIT database. The results obtained show the robustness of the proposed approach.

Keywords

Handwriting recognition Convolutional Neural Networks Hidden Markov Models 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mustapha Amrouch
    • 1
    Email author
  • Mouhcine Rabi
    • 2
  • Youssef Es-Saady
    • 2
  1. 1.Laboratory IRF-SIC, Faculty of SciencesIbn Zohr UniversityAgadirMorocco
  2. 2.Laboratory IRF-SICIbn Zohr UniversityAgadirMorocco

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