This paper comprehensively surveys recent advances in hand gesture and 3D hand pose recognition from various camera inputs, including RGB images, depth images, and videos from monocular or multi-lens cameras. This survey addresses the lack of a comprehensive survey covering recent research trends, available solutions, and benchmark datasets in a field of hand gesture recognition that is growing in importance due to the growing demand for human-computer interaction. We review the differing methodological requirements of various approaches, provide an overview of widely used datasets, and detail their key characteristics and areas of application. Finally, we highlight open challenges, such as achieving robust recognition in real-world environments, handling occlusion, ensuring generalization across diverse users, and addressing computational efficiency for real-time applications, suggesting future research directions.
