Open Access
ARTICLE
Abstract
(Aim) Chinese sign language is an essential tool for hearing-impaired to live, learn and communicate in deaf
communities. Moreover, Chinese sign language plays a significant role in speech therapy and rehabilitation. Chinese
sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society. Similar to the research of many biomedical
image processing (such as automatic chest radiograph processing, diagnosis of chest radiological images, etc.),
with the rapid development of artificial intelligence, especially deep learning technologies and algorithms, sign
language image recognition ushered in the spring. This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network.
(Method) Three different combinations of
blocks: Conv-BN-ReLU-Pooling, Conv-BN-ReLU, Conv-BN-ReLU-BN were employed, including some advanced
technologies such as batch normalization, dropout, and Leaky ReLU. We proposed an optimized convolutional
neural network to identify 1320 sign language images, which was called as CNN-CB method. Totally ten runs were
implemented with the hold-out randomly set for each run.
(Results) The results indicate that our CNN-CB method
gained an overall accuracy of 94.88 ± 0.99%.
(Conclusion) Our CNN-CB method is superior to thirteen state-ofthe-art methods: eight traditional machine learning approaches and five modern convolutional neural network
approaches
Keywords
Cite This Article
Gao, Y., Zhang, Y., Jiang, X. (2022). An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification.
CMES-Computer Modeling in Engineering & Sciences, 132(1), 95–117. https://doi.org/10.32604/cmes.2022.019970
Citations