Yueqin Sheng¹, Qunpo Liu¹, Ruxin Ga¹, Naohiko Hanajima^2
1School of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, Henan 454003, China
²College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto-cho, Hokkaido, Hokkaido 050-8585, Japan
pp. 268–274
ABSTRACT
Sign language is an important communication tool for deaf and hearing-impaired people. The study of sign language recognition can not only promote the communication between deaf-mutes and normal people, but also push the development of intelligent human-computer interaction. Sign language recognition based on deep learning has advantages in processing large scale dataset. Most of them use 3D convolution, which is not conducive to optimization. In this paper, an improved (2+1)D-ResNet model is proposed for isolated word recognition. The model convolves the video frame sequence in space and time dimensions and optimizes the parameters respectively. Based on CELU activation function, the accuracy of sign language recognition is improved effectively. The validity of proposed algorithm is verified on CSL dataset..

ARTICLE INFO
Article History
Received 24 November, 2021
Accepted 18 September 2022

Keywords
Sign language recognition
(2+1)D convolution
3D convolution
CELU activation function


JRNAL9310

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