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This paper is concerned with the video-based recognition of isolated signs. Concentrating on the manual parameters of sign language, the system aims for the signer dependent recognition of 262 different signs. For hidden Markov modelling a sign is considered a doubly stochastic process, represented by an unobservable state sequence. The observations emitted by the states are regarded as feature vectors, that are extracted from video frames. The system achieves recognition rates up to 94%.
Grobel et al. (Sat,) studied this question.
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