Enhancing Human-Computer Interaction with Hand Gesture Recognition

A common means of nonverbal expression is realized through a hand gesture recognition system, offering a contemporary and natural mode of communication. This technology finds wide-ranging applications in sign language interpretation and human-computer interaction, bridging the physical and digital realms. Enhancing user-computer interactions by increasing the computer’s receptivity to user demands is the fundamental aim of human-computer interaction. Human-computer interaction has grown in significance due to the widespread use of computers in society. It’s commonly accepted that when computer, communication, and display technologies advance even more, the current state of HCI practices could eventually impede the efficient use of the information flow that is already accessible. This work aims to explain a new method of hand gesture identification that relies on the detection of certain shape-based elements. Features are extracted using the Histogram of Gradients feature descriptor, and SVMs trained on HOG features provide $87.4 \%$ accurate results. In order to properly evaluate the efficacy of the recommended method, Metrics such as F-Score, Precision, and Recall provide a more nuanced view of the model’s performance than merely accuracy.