Exploring Models Approach for Real-Time Hand Gesture Recognition

Real-time hand gesture recognition technology has become an important research area in Human-Computer Interaction (HCI) due to its ability to enable more natural and intuitive interactions between humans and digital devices. By relying on hand gestures as input, this technology offers a new way to control devices without physical contact. In recent years, deep learning and computer vision-based approaches have developed rapidly, making significant contributions to improving the accuracy and speed of hand gesture recognition systems. Using the Systematic Literature Review (SLR) method, this study identifies and analyzes research trends, model architectures and algorithm-based approaches, and challenges faced in the development of this technology. This study also analyzes various technical challenges, including such as handling complex backgrounds, varying lighting conditions, and variations in hand shape and size, are also analyzed to illustrate the difficulties in creating a reliable and responsive system. The purpose of this review is to provide a comprehensive overview of current research trends, identify gaps that still need to be filled, and make recommendations for future research and development directions. Through this analysis, it is hoped that this research can help researchers and practitioners understand the existing developments and challenges and encourage innovation for more efficient and effective applications to assist people with disabilities.