IDEAS home Printed from https://ideas.repec.org/a/etm/ijsrst/v13y2026i3id1617.html

Vision Walk: AI-Driven Real-Time Visual Assistance System for Visually Impaired Navigation

Author

Listed:
  • Jitesh Sandesh Kadam
  • Vaibhav Vasant Joyashi
  • Waman R. Parulekar

Abstract

Visually impaired individuals face significant chal-lenges in perceiving their surroundings, detecting obstacles, read-ing text, and navigating safely. Traditional aids like white canes offer limited tactile feedback and lack contextual information. This paper presents Vision Walk, an AI-driven real-time visual assistance system that combines YOLOv8 for object detection, Tesseract OCR for text recognition, and spatial reasoning within a Flask-based web framework. The system operates entirely through a mobile browser, requiring no installation, and de-livers natural-language audio descriptions with directional cues (left, center, right). Experiments demonstrate that Vision Walk achieves object detection inference times of 40–80 ms, OCR processing of 70–140 ms, and end-to-end latency of 120–250 ms, making it suitable for real-time navigation. A user study with 12 visually impaired participants shows significant improvement in environmental awareness and navigation confidence. The system’s browser-based architecture ensures platform independence and cost-effectiveness, presenting a scalable solution for assistive technology.

Suggested Citation

  • Jitesh Sandesh Kadam & Vaibhav Vasant Joyashi & Waman R. Parulekar, 2026. "Vision Walk: AI-Driven Real-Time Visual Assistance System for Visually Impaired Navigation," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(3), pages 415-424, June.
    • Handle: RePEc:etm:ijsrst:v13:y2026:i3:id:1617
    DOI: 10.32628/IJSRST26133160
    as

    Download full text from publisher

    File URL: https://ijsrst.com/home/article/view/IJSRST26133160
    File Function: Abstract page
    Download Restriction: no
    File URL: https://ijsrst.com/home/article/download/IJSRST26133160/IJSRST26133160
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRST26133160?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:etm:ijsrst:v13:y2026:i3:id:1617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (email available below). General contact details of provider: https://ijsrst.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.