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Real Time Isolated Turkish Sign Language Recognition from Video Using Hidden Markov Models with Global Features

  • Hakan Haberdar
  • Songül Albayrak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3733)

Abstract

This paper introduces a video based system that recognizes gestures of Turkish Sign Language (TSL). Hidden Markov Models (HMMs) have been applied to design a sign language recognizer because of the fact that HMMs seem ideal technology for gesture recognition due to its ability of handling dynamic motion. It is seen that sampling only four key-frames is enough to detect the gesture. Concentrating only on the global features of the generated signs, the system achieves a word accuracy of 95.7%.

Keywords

Hide Markov Model Global Feature Gesture Recognition Sign Language Recognition American Sign 
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 2005

Authors and Affiliations

  • Hakan Haberdar
    • 1
  • Songül Albayrak
    • 1
  1. 1.Department of Computer EngineeringYildiz Technical UniversityIstanbulTurkey

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