American Sign Language Detection Using Machine Learning

This project introduces a system that helps deaf and mute individuals interact more easily by converting sign language into spoken or written words. It uses image processing and a deep learning model called Convolutional Neural Network (CNN) to identify hand gestures. The system analyzes each gesture through multiple layers to extract features and classify them accurately. This approach can improve communication in everyday situations and medical environments, and it can also be expanded into a mobile application for quick and wireless communication support. The human hand, being highly expressive, is frequently used not only for physical interaction but also for communication. For deaf and mute individuals, hand gestures form the foundation of sign language, which is essential for daily communication. Enabling computers to understand and interpret these gestures would mark a significant advancement in human-computer interaction. The development of such systems requires effective manipulation and processing of visual data