Sign Language Recognition System

Authors: Prathmesh Pradip Mane, Omkar Prabhu Gawade, Safin Mubarak Bagawan, Mrs. R. R. Jagatap

Abstract: Sign Language Recognition (SLR) is a critical bridge for communication between the hearing-impaired community and the rest of society. Traditional methods of interpretation are often scarce and expensive. This paper presents a comprehensive overview and technical methodology for developing a real-time Sign Language Recognition system leveraging Computer Vision. By utilizing Python as the core programming language and OpenCV for real-time image processing, coupled with deep learning frameworks such as TensorFlow/Keras and MediaPipe, the proposed system architecture can accurately detect, track, and classify hand gestures from a standard webcam feed. This paper explores the technological stack, the image processing pipeline, and the integration of Convolutional Neural Networks (CNNs) to achieve robust, real-time gesture translation