Abstract

Hand gestures are a powerful method of communication that serve as a bridge between humans and computers, enabling intuitive interaction. Hand Gesture Recognition (HGR) systems aim to support this vision but face several challenges such as gesture irregularity, illumination variation, background interference, and computational complexity. This study evaluates 252 peer-reviewed articles published between 1995 and 2024, with a focus on input modalities, algorithmic approaches, benchmark datasets, application domains, and system-level challenges such as automation, scalability, generalization, and real-time performance.The evolution of HGR methods is categorized chronologically, beginning with early rulebased models, progressing through classical machine learning techniques such as SVM, KNN, and HMM, and advancing to deep learning frameworks including CNNs, RNNs, LSTMs, 3D CNNs, and Graph Convolutional Networks (GCNs). In recent years, hybrid and pretrained architectures including LSTM+3DCNN, MAE+STGCN, and Transformer-based models have been proposed to address existing limitations and improve performance. Various input modalities have been explored, including RGB image and video data, depth sensors, skeletal tracking, IMU, and EMG signals. Widely adopted benchmark datasets include SHREC, DHG- 14/28, and NVGesture. A temporal classification framework is introduced to segment the progression of HGR technologies across decades. The study highlights key trends, technological advancements, and unresolved challenges, offering insights that may guide the development of accurate, efficient, and user-centric HGR systems, particularly in mobile and embedded computing contexts.

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