Real-Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges

Abstract

Real-time Hand Gesture Recognition (HGR) has emerged as a vital technology in human-computer interaction, offering intuitive and natural ways for users to interact with computer-vision systems. This comprehensive review explores the advancements, challenges, and future directions in real-time HGR. Various HGR-related technologies have also been investigated, including sensors and vision technologies, which are utilized as a preliminary step in acquiring data in HGR systems. This paper discusses different recognition approaches, from traditional handcrafted feature methods to state-of-the-art deep learning techniques. Learning paradigms have been analyzed such as supervised, unsupervised, transfer, and adaptive learning in the context of HGR. A wide range of applications has been covered, from sign language recognition to healthcare and security systems. Despite significant developments in the computer vision domain, challenges remain in areas such as environmental robustness, gesture complexity, computational efficiency, and user adaptability. Lastly, this paper concludes by highlighting potential solutions and future research directions trying to develop more robust, efficient, and user-friendly real-time HGR systems.