Open Access

ARTICLE

An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification

Yalan Gao, Yanqiong Zhang, Xianwei Jiang^*

Nanjing Normal University of Special Education, Nanjing, 210038, China

* Corresponding Author: Xianwei Jiang. Email:

(This article belongs to the Special Issue: Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)

Computer Modeling in Engineering & Sciences 2022, 132(1), 95-117. https://doi.org/10.32604/cmes.2022.019970

Received 29 October 2021; Accepted 15 December 2021; Issue published 02 June 2022

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

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Citations