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Chinese Sign Language Fingerspelling Recognition via Six-Layer Convolutional Neural Network with Leaky Rectified Linear Units for Therapy and Rehabilitation

journal contribution
posted on 2020-03-27, 12:29 authored by Xianwei Jiang, Yu-Dong Zhang
With hundreds of millions of hearing-impaired people around the world, sign language is considered as an effective solution to help bring the patients into the society. Besides, sign language is important in speech therapy and rehabilitation. However, it is not easy to learn. Automatic sign language recognition has become the focus of attention, which can help save labor resources in hospitals and certain occasions. Based on computer vision, we proposed a six-layer deep convolutional neural network with batch normalization, leaky ReLU and dropout techniques to identify Chinese sign language fingerspelling. Experiments showed that our approach achieved an overall accuracy of 88.10 ± 1.48%. The result is superior to three state-of-the-art approaches.

History

Citation

Journal of Medical Imaging and Health Informatics, Volume 9, Number 9, December 2019, pp. 2031-2090(60)

Author affiliation

Department of Informatics

Published in

JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS

Volume

9

Issue

9

Pagination

2031 - 2038 (8)

Publisher

AMER SCIENTIFIC PUBLISHERS

issn

2156-7018

eissn

2156-7026

Copyright date

2019

Available date

2019-12-01

Publisher version

https://www.ingentaconnect.com/content/asp/jmihi/2019/00000009/00000009/art00037

Notes

the publisher does not allow archiving of the accepted manuscript.

Language

English