Sign Language Recognition System using Machin...

Sign Language Recognition System using Machine Learning

Author Name : Dr. D. V. Rojatkar, Viraj Dilip Kadu

ABSTRACT Sign language serves as a crucial mode of communication for the deaf and hard- of-hearing community, yet barriers in communication with the hearing population persist. This project presents a Sign Language Recognition System utilising machine learning techniques to bridge this gap, enhancing accessibility and inclusivity.Key features include high recognition accuracy, real-time processing, and an intuitive user interface that supports seamless interaction. The implementation showcases the potential of machine learning in advancing assistive technologies, offering a practical solution to improve communication for the deaf and hard-of-hearing community. Future enhancements aim to incorporate advanced deep learning algorithms and expand the gesture database to cover more sign languages and dialects, further increasing the system’s utility and reach.This project introduces a Sign Language Recognition System leveraging machine learning frameworks such as TensorFlow and Scikit Learn to mitigate these challenges and promote inclusivity. Instead of convolutional neural networks, the system employs MediaPipe, a robust framework for real-time hand and body motion tracking, to interpret and translate sign language gestures into textual or spoken language in real-time. Trained on an extensive dataset of various sign languages and gestures, the model demonstrates adapt- ability accommodating different users and contexts.