A Comprehensive Analysis of Recognition of Hand Gestures using Machine Learning - Shivani, Shivani; Gupta, Satinder Bal (UI Scholars Hub, 2025)
Hand gestures are a natural means of conveying information and thus, there is an increasing interest in utilizing gestures for communication with computers. This study focuses on systematically reviewing different machine learning algorithms while assessing their working mechanisms and accuracy. Articles were analyzed for comparing the performance of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). In accordance with input data, intricacy of gestures, processing resources, and real-time demands, the study shows that each technique has distinct advantages and disadvantages. RNN showed the best accuracy of 99.28% in recognizing dynamic gestures, indicating that it can be employed in applications that need high accuracy. CNN also performed well in recognizing static gestures and provide an accuracy of 93.61% accuracy. In order to improve human-machine interaction through efficient hand gesture detection, this systematic and comprehensive analysis offers some insight into the trade-offs between choice of algorithm and performance.