Translator of Indonesian Sign Language Video using Convolutional Neural Network with Transfer Learning
DOI:
https://doi.org/10.24002/ijis.v5i1.5865Abstract
Sign language is a language used to communicate by utilizing gestures and facial expressions. This study focuses on classification of Bahasa Isyarat Indonesia (BISINDO). There are still many people who have difficulty communicating with the deaf people. This study builds video-based translator system using Convolutional Neural Network (CNN) with transfer learning which is commonly used in computer vision especially in image classification. Transfer learning used in this study are a MobileNetV2, ResNet50V2, and Xception. This study uses 11 different commonly used vocabularies in BISINDO. Predictions will be made in real-time scenario using a webcam. In addition, the system given good results in the experiment with an interaction approach between one pair of deaf and normal people. From all the experiments, it was found that the Xception architectures has the best F1 Score of 98.5%.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Indonesian Journal of Information Systems as journal publisher holds copyright of papers published in this journal. Authors transfer the copyright of their journal by filling Copyright Transfer Form and send it to Indonesian Journal of Information Systems.

Indonesian Journal of Information Systems is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.