Tribal Sign Translation System for Common People

Abstract

Communication between tribal communities and the general population is often difficult due to the use of unique hand signs and gestures that are not commonly understood outside their communities. To bridge this communication gap, this project introduces a Tribal Sign Translation System that converts tribal hand gestures into easily understandable text or voice output. The system captures images using a camera and processes them with OpenCV, applying techniques such as Gaussian filtering and image normalization to improve image clarity and consistency. For gesture recognition, it utilizes MobileNetV2, a lightweight and efficient deep learning model based on Convolutional Neural Networks (CNN), which ensures accurate and fast detection of hand signs. Additionally, the system can be enhanced with real-time translation capabilities, user-friendly interfaces, and support for multiple tribal gesture datasets to improve usability and inclusivity. It also has the potential to integrate text-to-speech functionality for voice output, making communication even more accessible. Overall, this solution aims to promote better interaction, preserve tribal communication methods, and enable seamless understanding between tribal and non-tribal communities.