Skip to main content

A Real-Time Large Vocabulary Recognition System for Chinese Sign Language

  • Conference paper
  • First Online:
Gesture and Sign Language in Human-Computer Interaction (GW 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2298))

Included in the following conference series:

Abstract

The major challenge that faces Sign Language recognition now is to develop methods that will scale well with increasing vocabulary size. In this paper, a real-time system designed for recognizing Chinese Sign Language (CSL) signs with a 5100 sign vocabulary is presented. The raw data are collected from two CyberGlove and a 3-D tracker. An algorithm based on geometrical analysis for purpose of extracting invariant feature to signer position is proposed. Then the worked data are presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, some useful new ideas are proposed in design and implementation, including modifying the transferring probability, clustering the Gaussians and fast matching algorithm. Experiments show that techniques proposed in this paper are efficient on either recognition speed or recognition performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. Charayaphan, A. Marble: Image processing system for interpreting motion in American Sign Language. Journal of Biomedical Engineering, 14(1992) 419–425.

    Article  Google Scholar 

  2. T. Starner: Visual recognition of American Sign Language using hidden Markov models. Master’s thesis, MIT Media Laboratory, July. 1995.

    Google Scholar 

  3. S. S. Fels, G. Hinton: GloveTalk: A neural network interface between a DataDlove and a speech synthesizer. IEEE Transactions on Neural Networks 4(1993) 2–8.

    Article  Google Scholar 

  4. S. Sidney Fels: Glove.TalkII: Mapping hand gestures to speech using neural networks-An approach to building adaptive interfaces. PhD thesis, Computer Science Department, University of Torono, 1994.

    Google Scholar 

  5. Tomoichi Takahashi, Fumio Kishino: Gesture coding based in experiments with a hand gesture interface device. SIGCHI Bulletin (1991) 23(2) 67–73.

    Article  Google Scholar 

  6. Yanghee Nam, K. Y. Wohn: Recognition of space-time hand-gestures using hidden Markov model. To appear in ACM Symposium on Virtual Reality Software and Technology (1996).

    Google Scholar 

  7. R. H. Liang, M. Ouhyoung: A real-time continuous gesture recognition system for sign language. In Proceeding of the Third International Conference on Automatic Face and Gesture Recognition, Nara, Japan (1998) 558–565.

    Google Scholar 

  8. Kirsti Grobel, Marcell Assan: Isolated sign language recognition using hidden Markov models. In Proceedings of the International Conference of System,Man and Cybernetics (1996) 162–167.

    Google Scholar 

  9. Christian Vogler, Dimitris Metaxas: Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In Proceedings of the IEEE International Confference on Systems, Man and Cybernetics, Orlando, FL (1997) 156–161.

    Google Scholar 

  10. Christian Vogler, Dimitris Metaxas: ASL recognition based on a coupling between HMMs and 3D motion analysis. In Proceedings of the IEEE International Conference on Computer Vision, Mumbai, India (1998) 363–369.

    Google Scholar 

  11. Christian Vogler, Dimitris Metaxas: Toward scalability in ASL Recognition: Breaking Down Signs into Phonemes. In Proceedings of Gesture Workshop, Gif-sur-Yvette, France (1999) 400–404.

    Google Scholar 

  12. Wen Gao, Jiyong Ma, Jiangqin Wu, Chunli Wang: Large Vocabulary Sign Language Recognition Based on HMM/ANN/DP. International Journal of Pattern Recognition and Artificial Intelligence, Vol. 14, No. 5 (2000) 587–602.

    Article  Google Scholar 

  13. L. Rabiner, B. Juang: Fundamentals of Speech Recognition. Publishing Company of TsingHua University.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chunli, W., Wen, G., Jiyong, M. (2002). A Real-Time Large Vocabulary Recognition System for Chinese Sign Language. In: Wachsmuth, I., Sowa, T. (eds) Gesture and Sign Language in Human-Computer Interaction. GW 2001. Lecture Notes in Computer Science(), vol 2298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47873-6_9

Download citation

  • DOI: https://doi.org/10.1007/3-540-47873-6_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43678-2

  • Online ISBN: 978-3-540-47873-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics