artificial intelligence

Audio To Sign Conversion and Hand Gesture Recognition

Architechture

Every language has its own syntax and rules for constructing meaningful statements. Similarly, sign language uses specific signs to enable communication for deaf and mute individuals. However, understanding and learning sign language can be challenging for those unfamiliar with it. When a hearing individual interacts with a hearing-impaired person, mutual understanding may not occur as neither party may fully understand the other's mode of communication. This highlights the need for a solution to bridge this communication gap without requiring everyone to learn sign language.

To address this, we propose a desktop-based application developed using Python and powered by Deep Learning. The technology leverages a Convolutional Neural Network (CNN) to analyze camera feeds and detect hand gestures. The model accepts inputs in both image and speech formats, converting hand gestures into text and translating audio into sign language. Additionally, it can recognize letters traced in the air. The output is displayed on the user's desktop as text and images, enabling seamless communication.

The primary objective of this system is to bridge the communication gap between the general population and the deaf or mute community. By integrating audio-to-sign conversion and hand gesture recognition, this project offers a practical and impactful solution for fostering better understanding and inclusion.

Involves:

Data Collection

Preprocessing

Hand Gesture Recognition

Audio-to-Text Conversion