Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
BT Technology Journal
A Survey of Longest Common Subsequence Algorithms
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
X3D: Extensible 3D Graphics for Web Authors
X3D: Extensible 3D Graphics for Web Authors
Accessible 3D signing avatars: the Tunisian experience
Proceedings of the International Cross-Disciplinary Conference on Web Accessibility
Increasing adaptability of a speech into sign language translation system
Expert Systems with Applications: An International Journal
A Review on 3D Signing Avatars: Benefits, Uses and Challenges
International Journal of Multimedia Data Engineering & Management
A new approach for animating 3d signing avatars
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
An inverse kinematics based approach for animating 3d signing avatars
Journal of Mobile Multimedia
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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.