Advancements in Machine Learning Techniques for Hand Gesture-Based Sign Language Recognition: A Comprehensive Review

Abstract

Sign Language Recognition (SLR) serves as a pivotal application of machine learning (ML) and deep learning (DL), enabling seamless automated communication between individuals with hearing impairments and the hearing population. Globally, there are approximately 7, 000 unique sign languages (SLs), characterized by diverse hand gestures, body movements, and facial expressions. These inherent variations add complexity to SLR systems, driving researchers to develop automated SLR (ASLR) frameworks to facilitate effective communication. To address the challenges posed by these variations, ASLR systems employ a range of advanced ML and DL methodologies to enhance accuracy. This study conducted a comprehensive review of 988 research articles retrieved from the SCOPUS database over the past two decades, employing relevant keywords to identify and analyze the prevailing trends in SLR research. The review provides a detailed evaluation of cutting-edge ML and DL techniques for hand gesture-based SLR, covering key aspects such as image acquisition, pre-processing, segmentation, feature extraction, and classification. The findings highlight that ensemble learning methods and transformer-based models outperform traditional approaches in terms of accuracy and robustness. Additionally, this study outlines critical challenges, open research questions, and potential future directions, offering valuable insights into advancing this field.