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ID 114106
Author
Keywords
Japanese sign language
gathered image
mean image
convolutional neural network
Content Type
Journal Article
Description
This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words.
Journal Title
International Journal of Advances in Intelligent Informatics
ISSN
24426571
25483161
Publisher
Universitas Ahmad Dahlan
Volume
5
Issue
3
Start Page
243
End Page
255
Published Date
2019-10-29
Rights
This is an open access article under the CC–BY-SA license(https://creativecommons.org/licenses/by-sa/4.0/).
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology