Definition and recovery of kinematic features for recognition of American sign language movements
Image and Vision Computing
Video-based signer-independent Arabic sign language recognition using hidden Markov models
Applied Soft Computing
Modelling and recognition of the linguistic components in American Sign Language
Image and Vision Computing
A Chinese sign language recognition system based on SOFM/SRN/HMM
Pattern Recognition
3-D hand trajectory recognition for signing exact English
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Low cost remote gaze gesture recognition in real time
Applied Soft Computing
Rule-based trajectory segmentation for modeling hand motion trajectory
Pattern Recognition
Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
Artificial Intelligence Review
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Sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. State-of-the-art sign language recognition should be able to solve the signer-independent continuous problem for practical applications. A divide-and-conquer approach, which takes the problem of continuous Chinese Sign Language (CSL) recognition as subproblems of isolated CSL recognition, is presented for signer-independent continuous CSL recognition in this paper. In the proposed approach, the improved simple recurrent network (SRN) is used to segment the continuous CSL. The outputs of SRN are regarded as the states of hidden Markov models (HMM) in which the Lattice Viterbi algorithm is employed for searching the best word sequence. Experimental results show that SRN/HMM approach has better performance than the standard HMM.