Hand gesture coding based on experiments using a hand gesture interface device
ACM SIGCHI Bulletin
Fundamentals of speech recognition
Fundamentals of speech recognition
Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Glove-talkii: mapping hand gestures to speech using neural networks. an approach to building adaptive interfaces
ASL Recognition Based on a Coupling Between HMMs and 3D Motion Analysis
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Real-Time Large Vocabulary Continuous Recognition System for Chinese Sign Language
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Generating Data for Signer Adaptation
Gesture-Based Human-Computer Interaction and Simulation
Signer adaptation based on etyma for large vocabulary Chinese sign language recognition
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Recognition of dynamic gestures in arabic sign language using two stages hierarchical scheme
International Journal of Knowledge-based and Intelligent Engineering Systems
Re-sampling for chinese sign language recognition
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
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The major challenge that faces Sign Language recognition now is to develop methods that will scale well with increasing vocabulary size. In this paper, a real-time system designed for recognizing Chinese Sign Language (CSL) signs with a 5100 sign vocabulary is presented. The raw data are collected from two CyberGlove and a 3-D tracker. An algorithm based on geometrical analysis for purpose of extracting invariant feature to signer position is proposed. Then the worked data are presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, some useful new ideas are proposed in design and implementation, including modifying the transferring probability, clustering the Gaussians and fast matching algorithm. Experiments show that techniques proposed in this paper are efficient on either recognition speed or recognition performance.