GESTURIFY-SIGNS THAT SPEAKS VOLUME

Sign language serves as the primary mode of communication for the deaf and hard-of-hearing community, yet a significant communication gap exists with the hearing population.Bridging this gap requires effective, accessible, and real-time translation systems.This paper presents Gesturify, a novel, lightweight, and robust Sign Language Recognition (SLR) system designed to operate on standard consumer hardware without the need for specialized sensors or data gloves.The proposed system leverages the Google Media Pipe framework for high-fidelity hand landmark detection and employs a k-Nearest Neighbors (k-NN) classifier for gesture recognition.We propose a hybrid microservices architecture that combines the asynchronous event-driven capabilities of a Node.jsbackend with the scientific computing power of a Python Flask inference server.Furthermore, we present a comprehensive evaluation of the system's performance, achieving an accuracy of 94.5% on a dataset of static American Sign Language (ASL) gestures with an average latency of 33ms per frame.We also discuss the limitations of current vision-based approaches and propose a road map for future enhancements using temporal deep learning models.