BISINDO Sign Language Recognition: A Systematic Literature Review of Deep Learning Techniques for Image Processing

Abstract

This study uses the Systematic Literature Review (SLR) method to examine the application of deep learning techniques focusing on BISINDO (Bahasa Isyarat Indonesia) image recognition. This is crucial for enhancing communication accessibility for the hearing-impaired community. The SLR process involves three stages: planning, conducting, and reporting. During the planning stage, research topics, questions, and search criteria are established, while the conducting stage involves comprehensive article retrieval and rigorous filtering. In the reporting stage, the study highlights the significance of various deep learning methodologies, including the implementation of several algorithms that ace in image recognition. For example, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and a combination of the two methods, each with unique advantages and limitations. Along with that, this paper aims to find gaps in previous research and act as a guide for future deep learning model development. Moreover, the research outlines the development of a high-performance model, emphasizing key phases such as image augmentation and data preprocessing, as well as model optimization. These efforts contribute to a better understanding of BISINDO image recognition, offering valuable insights for researchers and practitioners aiming to support easier accessibility and communication for the hearing-impaired community through advanced deep learning approaches.