Self-organized language modeling for speech recognition
Readings in speech recognition
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Estimation of Body Regions from Video Images
Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
ARGo: An Architecture for Sign Language Recognition and Interpretation
Proceedings of Gesture Workshop on Progress in Gestural Interaction
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Research on Computer Science and Sign Language: Ethical Aspects
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
Video-based signer-independent Arabic sign language recognition using hidden Markov models
Applied Soft Computing
Recognition of dynamic gestures in arabic sign language using two stages hierarchical scheme
International Journal of Knowledge-based and Intelligent Engineering Systems
Multimodal continuous recognition system for greek sign language using various grammars
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
A hybrid HMM/DPA adaptive gesture recognition method
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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This paper describes the development of a video-based continuous sign language recognition system using Hidden Markov Models (HMM). The system aims for automatic signer dependent recognition of sign language sentences, based on a lexicon of 52 signs of German Sign Language. A single colour video camera is used for image recording. The recognition is based on Hidden Markov Models concentrating on manual sign parameters. As an additional component, a stochastic language model is utilised, which considers uni- and bigram probabilities of single and successive signs. The system achieves an accuracy of 95% using a bigram language model.