el 18: e2

Research Article

Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network

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  • @ARTICLE{10.4108/eai.12-10-2020.166555,
        author={Ya Gao and Chengchong Jia and Hongli Chen and Xianwei Jiang},
        title={Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network},
        journal={EAI Endorsed Transactions on e-Learning: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EL},
        year={2020},
        month={10},
        keywords={deaf-mute, sign language recognition, Chinese fingerspelling recognition, convolutional neural network, stochastic pooling, batch normalization technology, dropout method},
        doi={10.4108/eai.12-10-2020.166555}
    }
    
  • Ya Gao
    Chengchong Jia
    Hongli Chen
    Xianwei Jiang
    Year: 2020
    Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network
    EL
    EAI
    DOI: 10.4108/eai.12-10-2020.166555
Ya Gao1, Chengchong Jia1, Hongli Chen1, Xianwei Jiang1,2,*
  • 1: School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
  • 2: Joint Accessibility Key Laboratory, China Disabled Persons' Federation, Nanjing 210038, China
*Contact email: jxw@njts.edu.cn

Abstract

INTRODUCTION: Sign language is a form of communication and exchange of ideas by people who are hearing-impaired or unable to speak. Chinese fingerspelling is an important component of Chinese sign language, which is suitable for denoting terminology and using as the basis of gesture sign language learning. OBJECTIVES: We propose a nine-layer convolutional neural network (CNN) for the classification of Chinese sign language. METHODS: With self-learning and self-organization abilities, CNN is committed to processing data with similar network structure. CNN has a good application prospect in the aspect of image classification andplays a very important role in the classification of Chinese sign language. RESULTS: Through experiments on 1320 data samples of 30 categories, the results show that the classification accuracy based on the nine-layer convolutional neural network can reach up to 89.69± 2.10 %, it can be seen that this method can effectively classify Chinese gestures. CONCLUSION: We proposed a nine-layer convolutional neural network (CNN) that can classify Chinese sign language.