Video-based continuous sign language recognition using statistical methods

This paper deals with the development of a video-based recognition system of continuous sign language. The system aims for an automatic signer dependent recognition of sign language sentences, based on a lexicon of 97 signs of German Sign Language. The recognition system is based on hidden Markov models with one model for each sign. A single video camera is utilised for data acquisition. Beamsearch is employed for the recognition task. For a better result a language model is implemented, which is able to handle a-priori knowledge of the training corpus. Different results are given for a vocabulary of 52 respectively, 97 signs with different language models (Unigram and Bigram) employed. The system achieves all accuracy of 91.8% based on a lexicon of 97 signs without a language model and 93.2% with employed Bigrams