Hand Gesture Recognition Using Machine Learning Technique

Abstract

The proposed hand gesture recognition system utilizing machine learning techniques is designed in this paper. It aims to identify static hand gestures in real-time, employing a camera module for image capture. Image processing methods, including segmentation and feature extraction, are utilized to process the captured images. The system utilizes the extracted features to conduct training and testing on various machine learning models including Decision Trees, Random Forest, and Support Vector Machines. Moreover, the system is equipped with a user interface allowing customization of hand gestures for recognition. The experimental findings demonstrate the system's ability to achieve high accuracy in recognizing hand gestures, even in varying lighting and background noise conditions. Its applications range from human-computer interaction to sign language recognition and virtual reality control. These findings underscore the system's superior accuracy compared to other advanced methods, showcasing its potential applications in various fields such as human-computer interaction, virtual reality, and sign language recognition.