Bridging the Gap: Real-Time Sign Language Translation
Prof. S. R. Karjol, Mr. Mohammed Mujtaba M Mulla, Mr. Abhishek Channabasavaraj Kinagi, Mr. Avaneesh N Devareddi, Mr. Prajwal Siddappa Hallur
Deep Learning, Sign Language Recognition, Communication Accessibility, Real-Time Translation, Wearable Technology.
This research explores the development of a real-time sign language translation mobile application designed to bridge the communication gap for deaf and mute individuals. Leveraging cutting-edge machine learning algorithms and computer vision techniques, the application captures and translates sign language gestures into text, enhancing accessibility and inclusivity. The study reviews existing systems like Microsoft Kinect and SignAloud gloves, and various academic efforts employing convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to improve sign language recognition accuracy. Key features of the proposed solution include real-time video processing, accurate text translation, and a user-friendly interface, with a strong focus on privacy and security. Through meticulous development and rigorous testing, the application aims to empower deaf individuals to communicate seamlessly with the broader population, fostering greater social inclusion and participation. This project not only addresses immediate communication barriers but also sets the stage for future advancements in assistive technologies, contributing to a more inclusive society.
Article Details
Unique Paper ID: 165012

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2827 - 2834
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