Signer-Independent Continuous Sign Language Recognition Based on SRN/HMM

Abstract

A divide-and-conquer approach is presented for signer-independent continuous Chinese Sign Language(CSL) recognition in this paper. The problem of continuous CSL recognition is divided into the subproblems of isolated CSL recognition. The simple recurrent network (SRN) and the hidden Markov models(HMM) are combined in this approach. The improved SRN is introduced for segmentation of continuous CSL. Outputs of SRN are regarded as the states of HMM, and the Lattice Viterbi algorithm is employed to search the best word sequence in the HMM framework. Experimental results show SRN/HMM approach has better performance than the standard HMM one.