International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 4 (July-August 2026) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 4
July-August 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Vision Enhancement System Using Artificial Intelligence and Internet of Things for Visually Impaired Individuals

Author(s) Mr. Ritesh Chhajed, Mr. Advait Dahake, Mr. Hrishikesh Tambe, Prof. Ashwini Deshmukh
Country India
Abstract A visual disability can be navigation and the situational awareness and access to the important information. However, conventional devices can only provide limited assistance and cannot help users understand the context of their environment. Moreover, despite the availability of sophisticated solutions, some are expensive or require constant internet connectivity to the function. This study presents a new concept of an assistive device based on Artificial Intelligence and IoT for improved quality of life for visually impaired persons. The proposed system will be able to execute different functions, such as real-time object identification, calculating distances from obstacles, face detection, optical character recognition (OCR), and subsequent speech synthesis. In contrast to other existing devices, this solution offers a completely offline multi-modal platform, where the delivery of notifications will be optimized for auditory channel usage in accordance with priority rules. Experiments showed that the presented model can be achieved an object detection rate of around 95% and a face detection rate of 97.5%, while the total reaction time did not exceed by 1.5 seconds
Keywords Assistive Technology, Artificial Intelligence, Internet of Things, Computer Vision, YOLOv7, OCR, Text-to- Speech, Raspberry Pi.
Field Engineering
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-06-16
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals