Japanese sign language classification based on gathered images and neural networks

International Journal of Advances in Intelligent Informatics

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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
Date 2019-10-29
Type info:eu-repo/semantics/article

Format application/pdf
Identifier http://ijain.org/index.php/IJAIN/article/view/406
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 243-255
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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