Continuous sign language recognition using level building based on fast hidden Markov model

• HMM-based Level Building algorithm outperforms other methods.
• Performance rises when employing Grammar and sign length constraints.
• System runs faster by using a fast algorithm for HMM.

Sign sequence segmentation and sign recognition are two main problems in continuous sign language recognition (CSLR) system. In recent years, dynamic time warping based Level Building (LB-DTW) algorithm has successfully dealt with both two challenges simultaneously. However, there still exists two crucial problems in LB-DTW: low recognition performance due to bad similarity function and offline due to high computation. In this paper, we use hidden Markov model (HMM) to calculate the similarity between the sign model and testing sequence, and a fast algorithm for computing the likelihood of HMM is proposed to reduce the computation complexity. Furthermore, grammar constraint and sign length constraint are employed to improve the recognition rate and a coarse segmentation method is proposed to provide the maximal level number. In experiments with a KINECT dataset of Chinese sign language containing 100 sentences composed of 5 signs each, the proposed method shows superior recognition performance and lower computation compared to other existing techniques.