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

Gao, Ya and Jia, Chengchong and Chen, Hongli and Jiang, Xianwei (2021) Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network. EAI Endorsed Transactions on e-Learning, 7 (20). e2. ISSN 2032-9253

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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.

Item Type: Article
Uncontrolled Keywords: deaf-mute, sign language recognition, Chinese fingerspelling recognition, convolutional neural network, stochastic pooling, batch normalization technology, dropout method
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 12 Feb 2021 13:07
Last Modified: 12 Feb 2021 13:07
URI: https://eprints.eudl.eu/id/eprint/1141

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