Real time isolated turkish sign language recognition from video using hidden markov models with global features

Authors:
Hakan Haberdar;Songül Albayrak
Affiliations:
Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey;Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey
Venue:
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Year:
2005

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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%.