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Software-Based Malaysian Sign Language Recognition

  • Farrah Wong
  • G. Sainarayanan
  • Wan Mahani Abdullah
  • Ali Chekima
  • Faysal Ezwen Jupirin
  • Yona Falinie Abdul Gaus
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)

Abstract

This work presents the development of a software-based Malaysian Sign Language recognition system using Hidden Markov Model. Ninety different gestures are used and tested in this system. Skin segmentation based on YCbCr colour space is implemented in the sign gesture videos to separate the face and hands from the background. The feature vector of sign gesture is represented by chain code, distance between face and hands and tilting orientation of hands. This work has achieved recognition rate of 72.22%.

Keywords

Feature Vector Recognition Rate Sign Language False Recognition Chain Code 
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 2013

Authors and Affiliations

  • Farrah Wong
    • 1
  • G. Sainarayanan
    • 1
  • Wan Mahani Abdullah
    • 1
  • Ali Chekima
    • 1
  • Faysal Ezwen Jupirin
    • 1
  • Yona Falinie Abdul Gaus
    • 1
  1. 1.School of Engineering and Information TechnologyUniversiti Malaysia SabahKota KinabaluMalaysia

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