Real Time Hand Gesture Recognition Using CNN

Real-time hand gesture recognition has become a vital component of human- computer interaction, enabling users to communicate with machines more intuitively. This technology has numerous applications in virtual reality, gaming, healthcare, and assistive technologies. However, developing accurate and efficient hand gesture recognition systems remains a challenging task due to variations in hand shapes, lighting conditions, and occlusions. This paper presents a novel approach to real-time hand gesture recognition using Convolutional Neural Networks (CNNs). Our method involves training a CNN model on a large dataset of images representing various hand gestures. The model is designed to learn spatial features from the images, allowing it to recognize gestures accurately and efficiently. To evaluate our approach, we conduct experiments on a publicly available dataset of hand gestures, achieving an accuracy of G5.6%. Our method is also optimized for real-time performance, achieving a processing speed of 30 frames per second