(2+1)D-SLR: an efficient network for video sign language recognition

The most existing sign language recognition methods have made significant progress. However, there are still problems in the field of sign language recognition: Traditional SLR technology relies on external devices such as data gloves, position tracker, and has achieved limited success. Moreover, the current state-of-the-art vision-based technologies cannot be applied in practice due to the difficulty in balancing accuracy and speed, because most of them pay the cost of running time for better sign language classification accuracy. In this paper, we propose a (2+1)D-SLR network based on (2+1)D convolution, which is different from other methods in that the proposed network can achieve higher accuracy with a faster speed. Because (2+1)D-SLR can learn spatio-temporal features from the raw sign RGB frames. In addition, the existing Chinese sign language dataset is difficult to guarantee the personality differences between different sign language speakers and the presentation differences of the same presenter. Therefore, we propose a large-scale Chinese sign language video dataset called NCSL to solve this problem, including 300 different sign language vocabulary which demonstrated by 30 volunteers, 10 times each. We also validated our method on NCSL and another large-scale sign language dataset, i.e., LSA64, Achieved 96.4% and 98.7% accuracy, respectively, demonstrating that our method can not only achieve competitive accuracy but be much faster than current well-known sign language recognition methods.