Sign Language Recognition Based Communication System Using Machine Learning Algorithm for Vocally Impaired People

Our research presents a system designed to empower individuals who are deaf or vocally impaired by enabling seamless communication through sign language recognition. The system integrates advanced sensor technology, data processing, and machine learning to translate hand and finger movements into understandable gestures. One accelerometer and five flex sensors are strategically placed on the fingers to capture precise movements, which are then transmitted to a receiver unit. The data is processed using a MATLAB-based application that employs various machine learning models, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Ensemble methods. The system is trained on a dataset generated from these sensor readings, with each model evaluated for its accuracy in gesture recognition. Among the tested models, the Ensemble method achieved the highest classification accuracy of 94.6%, making it the most effective for real-time sign language recognition. This system not only bridges the communication gap for deaf- mute individuals but also represents a significant step forward in creating more inclusive technologies for society.