Transition movement models for large vocabulary continuous sign language recognition

  • Authors:

  • Wen Gao;Gaolin Fang;Debin Zhao;Yiqiang Chen

  • Affiliations:

  • Department of Computer Science, Harbin Institute of Technology, Harbin, China and Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Department of Computer Science, Harbin Institute of Technology, Harbin, China;Department of Computer Science, Harbin Institute of Technology, Harbin, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
  • Year:
  • 2004

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Abstract

The major challenges that sign language recognition (SLR) now faces are developing methods that solve large vocabulary continuous sign problems. In this paper, large vocabulary continuous SLR based on transition movement models is proposed. The proposed method employs the temporal clustering algorithm to cluster a large amount of transition movements, and then the corresponding training algorithm is also presented for automatically segmenting and training these transition movement models. The clustered models can improve the generalization of transition movement models, and are very suitable for large vocabulary continuous SLR. At last, the estimated transition movement models, together with sign models, are viewed as candidate models of the Viterbi search algorithm for recognizing continuous sign language. Experiments show that continuous SLR based on transition movement models has good performance over a large vocabulary of 5113 signs.