Real-Time Hand Gesture Recognition Using Convolutional Neural Networks: A Robust Approach for Diverse Applications. Hand gestures are fundamental to human communication and find extensive utility across various domains. However, the real-time recognition of hand gestures using computer vision algorithms poses significant challenges. While several algorithms have incorporated color and depth cameras to facilitate gesture recognition, achieving robust classification across diverse individuals remains a notable hurdle. In this paper, we present a novel algorithm that leverages Convolutional Neural Networks (CNNs) for real-time hand gesture recognition. Our CNN model is trained on a comprehensive dataset consisting of nine distinct hand gestures, each comprising 500 images. Through rigorous evaluation, our proposed algorithm achieves an impressive average accuracy of 98.76%, underscoring its potential to establish secure and user-friendly interfaces for a wide range of applications.