Implementation of Hand Gesture Recognition for Indonesian Sign Language based on MobileNetV2

Abstract

Communication is a fundamental human necessity; however, for the deaf community, barriers to interaction
with the general public remain a significant challenge due to limited literacy in sign language. This research aims to
implement a hand gesture recognition system capable of translating the alphabet of the Indonesian Sign Language System
(SIBI) in real-time. The MobileNetV2 architecture was selected as the base model due to its efficiency in processing data on
resource-constrained devices without significantly compromising accuracy. The methodology involves several crucial
stages, beginning with image pre-processing—including resizing and image normalization—to the application of data
augmentation strategies such as rotation, shifting, and brightness adjustment to enhance the model's generalization
capabilities in real-world conditions. The dataset comprises SIBI alphabet classifications from A to Z, collected with high
variability to minimize the risk of overfitting. The results demonstrate that the use of depthwise separable convolutions in
MobileNetV2 allows the system to perform gesture detection with high responsiveness and low computational overhead.
Through hyperparameter optimization, this model is expected to achieve optimal accuracy, providing a practical and
inclusive communication tool for the deaf community within social environments and public services.