Toward Automatic Sign Language Recognition from Web3D Based Scenes

  • Kabil Jaballah
  • Mohamed Jemni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6180)


This paper describes the development of a 3D continuous sign language recognition system. Since many systems like WebSign[1], Vsigns[2] and eSign[3] are using Web3D standards to generate 3D signing avatars, 3D signed sentences are becoming common. Hidden Markov Models is the most used method to recognize sign language from video-based scenes, but in our case, since we are dealing with well formatted 3D scenes based on H-anim and X3D standards, Hidden Markov Models (HMM) is a too costly double stochastic process. We present a novel approach for sign language recognition using Longest Common Subsequence method. Our recognition experiments were based on a 500 signs lexicon and reach 99 % of accuracy.


Sign Language X3D/VRML Gesture recognition Web3D scenes H-Anim Virtual reality 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jemni, M., Elghoul, O.: Towards Web-Based automatic interpretation of written text to Sign Language. In: ICTA 2007, Hammamet, Tunisia, April 2007, pp. 12–14 (2007)Google Scholar
  2. 2.
    Papadogiorgaki, M., Grammalidis, N., Makris, L., Sarris, N., Strintzis, M.G.: Vsign Project. Communication (September 20, 2002),
  3. 3.
    Ehrhardt, U., Davies, B.: A good introduction to the work of the eSIGN project. eSIGN Deliverable D7-2 (August 2004)Google Scholar
  4. 4.
    Cuxac, C.: La LSF, les voies de l’iconicité. Ophrys editions, Paris (2000) Google Scholar
  5. 5.
    Web3D consortium website,
  6. 6.
    Humanoid Animation Standard Group, Specification for a Standard Humanoid: H-Anim 1.1,
  7. 7.
    Brutzman, D., Ardly, L.: X3D Extensible 3D Graphics for Web Authors. Elsevier, Amsterdam (2007)Google Scholar
  8. 8.
    Vogler, C., Metaxas, D.: Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In: IEEE International Conference on Computational Cybernetics and Simulation, Oralando, FL (October 1997)Google Scholar
  9. 9.
    Starner, T., Weaver, J., Pentland, A.: Real-time American Sign Language Recognition using desk and wearable Computer based Video. J. IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)Google Scholar
  10. 10.
    Rabiner, L.: A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. Of the IEEE 77, 257–289 (1989)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Bergroth, L., Hakonen, H., Raita, T.: A Survey of Longest Common Subsequence Algorithms. J. SPIRE, 39–48 (2000)Google Scholar
  13. 13.
    Torgeson, W.S.: Multidimensional scaling of similarity. J. Psychometrika 379–393 (2006)Google Scholar
  14. 14.
    Deza, M., Deza, E.: Dictionary of Distances, Elsevier editions, Amsterdam (2006)Google Scholar
  15. 15.
    Jaballah, K., Jemni, M.: Automatic Sign Language Recognition using X3D/VRML Animated Humanoids. In: CVHI 2009, Wroclaw, Poland (April 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kabil Jaballah
    • 1
  • Mohamed Jemni
    • 1
  1. 1.UTIC Research LaboratoryHigh School Of Science and Techniques of TunisBab MnaraTunisia

Personalised recommendations