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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM35763

A Case Study on (EAVIBA) Enhanced Accessibility for Visually Impaired & Blind Artists using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM35763

Volume:

8 |

6 |

Jun 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM35763

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MZK

Mohammad Zia Karimi

DBKS

Dr. Brahmaleen K. Sidhu

DGG

Dr. Gaurav Gupta

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Abstract

This research paper introduces EAVIBA, a pioneering voice-driven interface tailored to empower visually impaired and blind individuals in artistic pursuits. Rooted in principles of accessible design, EAVIBA seamlessly integrates voice commands, text-to-speech functionality, and a customizable graphical user interface. The study delves into EAVIBA’s architecture, implementation of assistive technologies, and commitment to accessibility standards like the Web Content Accessibility Guidelines (WCAG). Through a robust iterative testing process, the paper evaluates the program, soliciting both qualitative and quantitative feedback on key aspects such as interface clarity, voice command recognition, and customization features. Objectives: The primary objectives of EAVIBA encompass developing an inclusive graphical user interface, implementing voice recognition and text-to-speech functionalities, providing creative tools and recommendations, enabling customization, and facilitating user feedback for continuous improvement. Method: The research employed a user-centric approach, incorporating principles of universal design in the graphical user interface. The integration of voice recognition and text-to-speech technologies, along with customizable features, was meticulously executed. Evaluation involved iterative user testing to gauge interface clarity, voice command recognition accuracy, and the efficacy of customization options. Findings: Quantitative data revealed a high degree of satisfaction among users, with an average recognition accuracy of voice commands exceeding 90%. Interface clarity received positive feedback, emphasizing the success of the high contrast mode and Blind User Mode. Users appreciated the customization options, with 80% indicating satisfaction with tailored voice commands. Novelty: EAVIBA’s novelty lies in its holistic approach, addressing accessibility and fostering creativity. The integration of voice-driven interactions, high contrast modes, and creative recommendations distinguishes EAVIBA from existing solutions, presenting a comprehensive platform for visually impaired artists.

KEYWORDS

Assistive technologies visually impaired blind artists voice-driven interface machine learning voice recognition.

Cite This Article

Mohammad Zia Karimi, Dr. Brahmaleen K. Sidhu, Dr. Gaurav Gupta, "A Case Study on (EAVIBA) Enhanced Accessibility for Visually Impaired & Blind Artists using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 6, Jun 2024. https://doi.org/10.55041/IJSREM35763

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Publication Date

11/5/2024

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Double-Blind Peer Review

Review Rounds: 2

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