Abstract
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.
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Jaballah, K., Jemni, M. (2010). Toward Automatic Sign Language Recognition from Web3D Based Scenes. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds) Computers Helping People with Special Needs. ICCHP 2010. Lecture Notes in Computer Science, vol 6180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14100-3_31
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DOI: https://doi.org/10.1007/978-3-642-14100-3_31
Publisher Name: Springer, Berlin, Heidelberg
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