Engineering, Mathematics and Computer Science Journal (EMACS)
Vol. 5 No. 3 (2023): EMACS

Deep Transfer Learning for Sign Language Image Classification: A Bisindo Dataset Study

Ika Dyah Agustia Rachmawati (Bina Nusantara University)
Rezki Yunanda (Bina Nusantara University)
Muhammad Fadlan Hidayat (Bina Nusantara University)
Pandu Wicaksono (Bina Nusantara University)

Article Info

Publish Date
30 Sep 2023

Abstract

This study aims to identify and categorize the BISINDO sign language dataset, primarily consisting of image data. Deep learning techniques are used, with three pre-trained models: ResNet50 for training, MobileNetV4 for validation, and InceptionV3 for testing. The primary objective is to evaluate and compare the performance of each model based on the loss function derived during training. The training success rate provides a rough idea of the ResNet50 model's understanding of the BISINDO dataset, while MobileNetV4 measures validation loss to understand the model's generalization abilities. The InceptionV3-evaluated test loss serves as the ultimate litmus test for the model's performance, evaluating its ability to classify unobserved sign language images. The results of these exhaustive experiments will determine the most effective model and achieve the highest performance in sign language recognition using the BISINDO dataset.

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Journal Info
Engineering, Mathematics and Computer Science Journal (EMACS)
Website

Abbrev

EMACS

Publisher

Universitas Bina Nusantara

Subject

Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

Description

Engineering, MAthematics and Computer Science (EMACS) Journal invites academicians and professionals to write their ideas, concepts, new theories, or science development in the field of Information Systems, Architecture, Civil Engineering, Computer Engineering, Industrial Engineering, Food ...