A SRN/HMM system for signer-independent continuous sign language recognition
Sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech. State-of-the-art sign language recognition should be able to solve the signer-independent continuous problem for practical applications. A divide-and-conquer approach, which takes the problem of continuous Chinese Sign Language (CSL) recognition as subproblems of isolated CSL recognition, is presented for signer-independent continuous CSL recognition. In the proposed approach, the improved simple recurrent network (SRN) is used to segment the continuous CSL. The outputs of SRN are regarded as the states of hidden Markov models (HMM) in which the Lattice Viterbi algorithm is employed for searching for the best word sequence. Experimental results show that the SRN/HMM approach has a better performance than the standard HMM.