Abstract
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
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Hienz, H., Bauer, B., Kraiss, K. (1999). HMM-Based Continuous Sign Language Recognition Using Stochastic Grammars. In: Braffort, A., Gherbi, R., Gibet, S., Teil, D., Richardson, J. (eds) Gesture-Based Communication in Human-Computer Interaction. GW 1999. Lecture Notes in Computer Science(), vol 1739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46616-9_17
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DOI: https://doi.org/10.1007/3-540-46616-9_17
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