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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. [ABSTRACT FROM AUTHOR]

• A sign language recognition framework based on global-local attention, including: • A Multiple Group Random Segment Sampling algorithm to extract the key frame clips; • A global Attention-Model based ResC3D network with an improved timing conversion layer; • A local SLR algorithm based on Mask RCNN hand detection network. Video-level sign language recognition is still a challenging task due to the influence of sign language-independent factors and timing requirements. This paper constructs a sign language recognition framework based on global-local feature description, and proposes a three-dimensional residual global network model with attention layer and a local network model based on target detection. The global feature description is based on the whole video behavior for time series modeling. The improved timing conversion layer is used to explore the timing information of different periods and learn the video representations of different timings. In the local module the hand is located through the target detection network to highlight its key role in the whole sign language behavior, which strengthens the category differences, and compensates the global network. Experiments on two well-known Chinese sign language datasets (SLR_Dataset and DEVSIGN_D) show that the proposed method can obtain higher recognition accuracy (respectively 89.2%, 91%) and better generalization performance. [ABSTRACT FROM AUTHOR]

The major challenges that sign language recognition (SLR) now faces are developing methods that solve large-vocabulary continuous sign problems. In this paper, transition-movement models (TMMs) are proposed to handle transition parts between two adjacent signs in large-vocabulary continuous SLR. For tackling mass transition movements arisen from a large vocabulary size, a temporal clustering algorithm improved from k-means by using dynamic time warping as its distance measure is proposed to dynamically cluster them; then, an iterative segmentation algorithm for automatically segmenting transition parts from continuous sentences and training these TMMs through a bootstrap process is presented. The clustered TMMs due to their excellent generalization are very suitable for large-vocabulary continuous SLR. Lastly, TMMs together with sign models are viewed as candidates of the Viterbi search algorithm for recognizing continuous sign language. Experiments demonstrate that continuous SLR based on TMMs has good performance over a large vocabulary of 5113 Chinese signs and obtains an average accuracy of 91.9%. [ABSTRACT FROM AUTHOR]

In sign language recognition (SLR), the major challenges now are developing methods that solve signer-independent continuous sign problems. In this paper, SOFM/HMM is first presented for modeling signer-independent isolated signs. The proposed method uses the self-organizing feature maps (SOFM) as different signers'' feature extractor for continuous hidden Markov models (HMM) so as to transform input signs into significant and low-dimensional representations that can be well modeled by the emission probabilities of HMM. Based on these isolated sign models, a SOFM/SRN/HMM model is then proposed for signer-independent continuous SLR. This model applies the improved simple recurrent network (SRN) to segment continuous sign language in terms of transformed SOFM representations, and the outputs of SRN are taken as the HMM states in which the lattice Viterbi algorithm is employed to search the best matched word sequence. Experimental results demonstrate that the proposed system has better performance compared with conventional HMM system and obtains a word recognition rate of 82.9% over a 5113-sign vocabulary and an accuracy of 86.3% for signer-independent continuous SLR. [Copyright &y& Elsevier]