Real-Time Sign Language Recognition System using Computer Vision and Deep Learning

Sign language serves as a communication method for individuals who are deaf or mute. Despite this, there are still communication challenges between those with hearing impairments and those without, primarily due to the absence of automated interpretation systems. This project introduces a real-time sign language recognition system that leverages computer vision and machine learning. The system utilizes alive camera feed to capture hand gestures, identifies hand landmarks with Media Pipe, and emp loys a trained Convolutional Neural Network (CNN) model to classifythese gestures. The identified gestures are then translated  into text, with suggestions for commonly used wordsprovided to enhance communicat ion efficiency. The system is designed to be user-friendly, affordable, and capable of operating in real time without the need for specialized hardware, thus aiding in closing the communication gap and fostering inclusivity.Key Words:Sign Language Recognition, Hand Gesture Recognition,Machine Learning, Deep Learning, CNN, Computer Vision,Media Pipe