Japanese sign language classification based on gathered images and neural networks

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Japanese sign language classification based on gathered images and neural networks
 
Creator Ito, Shin-ichi
Ito, Momoyo
Fukumi, Minoru
 
Subject Japanese sign language; Gathered image; Mean image; Convolutional neural network
 
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.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2019-10-29
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/406
10.26555/ijain.v5i3.406
 
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 243-255
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/406/ijain_vol5i3_p243-255
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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