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All Journal ComEngApp : Computer Engineering and Applications Journal
Anna Dwi Marjusalinah
Graduate Program in Computer Science, Faculty of Computer Science, Universitas Sriwijaya
Computer Science & IT Engineering

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Classification of Finger Spelling American Sign Language Using Convolutional Neural Network Anna Dwi Marjusalinah; Samsuryadi Samsuryadi; Muhammad Ali Buchari
Computer Engineering and Applications Journal Vol 10 No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.454 KB) | DOI: 10.18495/comengapp.v10i2.377

Abstract

Sign language is a combination of complex hand movements, body postures, and facial expressions. However, only a limited number of people can understand and use it. A computer aid sign language recognition with finger spelling style utilizing a convolutional neural network (CNN) is proposed to reduce the burden. We compared two CNN architectures such as Resnet 50, and DenseNet 121 to classify the American sign language dataset. Several data splitting proportions were also tested. From the experimental result, it is shown that the Resnet 50 architecture with 80:20 data splitting for training and testing indicates the best performance with an accuracy of 0.999913, sensitivity 0.998966, precision 0.998958, specificity 0.999955, F1-score 0.999913, and error 0.0000898.
Co-Authors Buchari, Muhammad Ali Samsuryadi Samsuryadi