Sign Language Recognition System integrated with a mobile application

Abstract

This project aims to develop an innovative Sign Language Recognition System integrated with a mobile application, utilizing state-of-the-art machine learning techniques. The project addresses the communication challenges faced by individuals with hearing impairments by providing a real-time, efficient, and user-friendly solution for sign language interpretation.

The proposed system leverages a comprehensive dataset for training a machine learning model, enabling it to accurately recognize a diverse range of sign language gestures. The mobile application acts as a seamless interface, allowing users to communicate through sign language effortlessly. The system's integration with a mobile platform enhances accessibility, enabling users to engage in conversations, access information, and participate in various daily activities.

Key features of the project include real-time gesture recognition, continuous learning and updating of the machine learning model for improved accuracy, and a user-friendly interface for both sign language users and those interacting with them. The successful implementation of this Sign Language Recognition System is expected to contribute significantly to fostering inclusivity and accessibility for individuals with hearing impairments.

Index Terms

Sign language recognition, Machine learning techniques, Mobile applications, Communication challenges, Hearing impairments, Real-time gesture recognition, Dataset, Accessibility, Inclusivity, Continuous learning, User-friendly interface, Integration, Mobile platform, Daily activities, Improved accuracy.