Enhanced Accessibility for Visually Impaired & Blind Artists (EAVIBA) using Machine Learning

Abstract

This research paper introduces EAVIBA, an innovative voice-driven interface aimed at empowering individuals with visual impairments and blindness in the domain of art and creativity. Rooted in principles of accessible design, EAVIBA integrates advanced features including voice commands, text-to-speech functionality, and a customizable graphical user interface, exemplifying a commitment to inclusive technology. The paper delves into the intricacies of EAVIBA's development, elucidating its architectural framework, seamless integration of assistive technologies, and adherence to recognized accessibility standards such as the Web Content Accessibility Guidelines (WCAG). Employing a comprehensive approach, the research paper outlines the iterative process of user testing, providing both qualitative and quantitative insights into critical aspects such as interface clarity, voice command recognition accuracy, and the efficacy of customization options. The results of these evaluations affirm EAVIBA's remarkable success in delivering an inclusive, accessible, and empowering environment for individuals with visual impairments to engage in artistic expression, thereby contributing significantly to the broader landscape of assistive technologies and accessible interfaces. Keywords: Visually impaired, blind artists, voice-driven interface, assistive technologies, voice recognition, machine learning.