AI-Driven Novel Approach for Enhancing E-Commerce Accessibility through Sign Language Integration in Web and Mobile Applications
Vishnu Ramineni, Balaji Shesharao Ingole, Manjunatha Sughaturu Krishnappa, Akshay Nagpal, Vivekananda Jayaram, Amey Ram Banarse, Darshan Mohan Bidkar, Nikhil Kumar Pulipeta · 2024
As e-commerce continues to grow, it is crucial to ensure accessibility for all users, including individuals with hearing impairments. Current web and mobile platforms often lack adequate support for sign language users, creating barriers to inclusivity. This paper introduces an innovative approach that leverages Artificial Intelligence (AI) and Machine Learning (ML) to enhance e-commerce accessibility by integrating sign language recognition. Our system employs real-time gesture recognition using computer vision and natural language processing to translate sign language gestures into text or voice commands, and vice versa, providing a seamless two-way interaction. Key components of the system include the development of a robust sign language dataset, optimization of machine learning models for gesture accuracy, and ensuring real-time responsiveness in web and mobile environments. Usability studies show significant improvements in accessibility for deaf and hard-of-hearing users, offering an inclusive shopping experience. This paper discusses the system architecture, challenges, and potential future enhancements, highlighting the impact of AI-driven solutions in creating more inclusive e-commerce platforms.