Signer-independent continuous sign language recognition based on SRN/HMM 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.

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  1. Beh, Jounghoon; Han, David; Ko, Hanseok: Rule-based trajectory segmentation for modeling hand motion trajectory (2014) ioport
  2. Kong, W. W.; Ranganath, Surendra: Towards subject independent continuous sign language recognition: a segment and merge approach (2014) ioport
  3. Caridakis, G.; Karpouzis, K.; Drosopoulos, A.; Kollias, S.: Non parametric, self organizing, scalable modeling of spatiotemporal inputs: the sign language paradigm (2012) ioport
  4. Flasiński, Mariusz; Myśliński, Szymon: On the use of graph parsing for recognition of isolated hand postures of Polish sign language (2010) ioport
  5. Ong, Sylvie C. W.; Ranganath, Surendra; Venkatesh, Y. V.: Understanding gestures with systematic variations in movement dynamics (2006)
  6. Drummond, Isabela; Sandri, Sandra: A clustering-based possibilistic method for image classification (2004)
  7. Gao, Wen; Fang, Gaolin; Zhao, Debin; Chen, Yiqiang: A Chinese sign language recognition system based on SOFM/SRN/HMM (2004)
  8. Fang, Gaolin; Gao, Wen; Wang, Zhaoqi: Incorporating linguistic structure into maximum entropy language models (2003)
  9. Fang, Gaolin; Gao, Wen; Chen, Xilin; Wang, Chunli; Ma, Jiyong: Signer-independent continuous sign language recognition based on SRN/HMM (2002)