Skip to main content

Sign Language Recognition

  • Chapter

Part of the book series: Signals and Communication Technology ((SCT))

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   299.00
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bahl, L., Jelinek, F., and Mercer, R. A Maximum Likelihood Approach to Continuous Speech Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(2):179–190, March 1983.

    Article  Google Scholar 

  2. Bauer, B. Erkennung kontinuierlicher Gebärdensprache mit Untereinheiten-Modellen. Shaker Verlag, Aachen, 2003.

    Google Scholar 

  3. Becker, C. Zur Struktur der deutschen Gebärdensprache. WVT Wissenschaftlicher Verlag, Trier (Germany), 1997.

    Google Scholar 

  4. Canzler, U. and Dziurzyk, T. Extraction of Non Manual Features for Videobased Sign Language Recognition. In The University of Tokyo, I. o. I. S., editor, Proceedings of IAPR Workshop on Machine Vision Applications, pages 318–321. Nara, Japan, December 2002.

    Google Scholar 

  5. Canzler, U. and Ersayar, T. Manual and Facial Features Combination for Videobased Sign Language Recognition. In 7th International Student Conference on Electrical Engineering, page IC8. Prague, May 2003.

    Google Scholar 

  6. Derpanis, K. G. A Review of Vision-Based Hand Gestures. Technical report, Department of Computer Science, York University, 2004.

    Google Scholar 

  7. Duda, R., Hart, P., and Stork, D. Pattern Classifikation. Wiley-Interscience, New York, 2000.

    Google Scholar 

  8. Fang, G., Gao, W., Chen, X., Wang, C., and Ma, J. Signer-Independent Continuous Sign Language Recognition Based on SRN/HMM. In Revised Papers from the International Gesture Workshop on Gestures and Sign Languages in Human-Computer Interaction, pages 76–85. Springer, 2002.

    Google Scholar 

  9. Hermansky, H., Timberwala, S., and Pavel, M. Towards ASR on Partially Corrupted Speech. In Proc. ICSLP’ 96, volume 1, pages 462–465. Philadelphia, PA, 1996.

    Google Scholar 

  10. Holden, E. J. and Owens, R. A. Visual Sign Language Recognition. In Proceedings of the 10th International Workshop on Theoretical Foundationsof Computer Vision, pages 270–288. Springer, 2001.

    Google Scholar 

  11. Jelinek, F. Statistical Methods for Speech Recognition. MIT Press, 1998. ISBN 0-262-10066-5.

    Google Scholar 

  12. Jones, M. and Rehg, J. Statistical Color Models with Application to Skin Detection. Technical Report CRL 98/11, Compaq Cambridge Research Lab, December 1998.

    Google Scholar 

  13. KaewTraKulPong, P. and Bowden, R. An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection. In AVBS01. 2001.

    Google Scholar 

  14. Liang, R. H. and Ouhyoung, M. A Real-time Continuous Gesture Interface for Taiwanese Sign Language. In UIST’ 97 Proceedings of the 10th Annual ACM Symposium on User Interface Software and Technology. ACM, Banff, Alberta, Canada, October 14–17 1997.

    Google Scholar 

  15. Murakami, K. and Taguchi, H. Gesture Recognition Using Recurrent Neural Networks. In Proceedings of the SIGCHI conference on Human factors in computingsystems, pages 237–242. ACM Press, 1991.

    Google Scholar 

  16. Ong, S. C. W. and Ranganath, S. Automatic Sign Language Analysis: A Survey and the Future Beyond Lexical Meaning. IEEE TPAMI, 27(6):873–8914, June 2005.

    Google Scholar 

  17. Parashar, A. S. Representation And Interpretation Of Manual And Non-manual InformationFor Automated American Sign Language Recognition. Phd thesis, Department of Computer Science and Engineering, College of Engineering, University of South Florida, 2003.

    Google Scholar 

  18. Porikli, F. and Tuzel, O. Human Body Tracking by Adaptive Background Models and Mean-Shift Analysis. Technical Report TR-2003-36, Mitsubishi Electric Research Laboratory, July 2003.

    Google Scholar 

  19. Sonka, M., Hlavac, V., and Boyle, R. Image Processing, Analysis and Machine Vision. Brooks Cole, 1998.

    Google Scholar 

  20. Starner, T., Weaver, J., and Pentland, A. Real-Time American Sign Language Recognition Using Desk and WearableComputer Based Video. IEEE Transactions on Pattern Analysis and Machine Intelligence,, 20(12):1371–1375, December 1998.

    Article  Google Scholar 

  21. Stauffer, C. and Grimson, W. E. L. Adaptive Background Mixture Models for Real-time Tracking. In Computer Vision and Pattern Recognition 1999, volume 2. 1999.

    Google Scholar 

  22. Stokoe, W. Sign language structure: An Outline of the Visual Communication Systems of the American Deaf. (Studies in Linguistics. Occasional Paper; 8) University of Buffalo, 1960.

    Google Scholar 

  23. Sutton, V. http://www.signwriting.org/, 2003.

    Google Scholar 

  24. Vamplew, P. and Adams, A. Recognition of Sign Language Gestures Using Neural Networks. In European Conference on Disabilities, Virtual Reality and AssociatedTechnologies. 1996.

    Google Scholar 

  25. Vogler, C. and Metaxas, D. Parallel Hidden Markov Models for American Sign Language Recognition. In Proceedings of the International Conference on Computer Vision. 1999.

    Google Scholar 

  26. Vogler, C. and Metaxas, D. Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes. In Braffort, A., Gherbi, R., Gibet, S., Richardson, J., and Teil, D., editors, The Third Gesture Workshop: Towards a Gesture-Based Communication in Human-Computer Interaction, pages 193–204. Springer-Verlag Berlin, Gif-sur-Yvette (France), 2000.

    Google Scholar 

  27. Welch, G. and Bishop, G. An Introduction to the Kalman Filter. Technical Report TR 95-041, Department of Computer Science, University of North Carolina at Chapel Hill, 2004.

    Google Scholar 

  28. Yang, M., Ahuja, N., and Tabb, M. Extraction of 2D Motion Trajectories and Its Application to HandGesture Recognition. IEEE Transactions On Pattern Analysis And Machine Intelligence, 24:1061–1074, 2002.

    Article  Google Scholar 

  29. Zieren, J. and Kraiss, K.-F. Robust Person-Independent Visual Sign Language Recognition. In Proceedings of the 2nd Iberian Conference on Pattern RecognitionandImage Analysis IbPRIA 2005, volume Lecture Notes in Computer Science. 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Zieren, J., Canzler, U., Bauer, B., Kraiss, KF. (2006). Sign Language Recognition. In: Kraiss, KF. (eds) Advanced Man-Machine Interaction. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-30619-6_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-30619-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30618-4

  • Online ISBN: 978-3-540-30619-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics