IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-032-11983-4_35.html

A Novel AI-Driven Assistive Framework for Enhanced Navigation and Text Recognition for Visually Impaired Using Smart Glasses

Author

Listed:
  • Priyanka Kumari

    (Nash Squared)

  • Ramy Hammady

    (University of Southampton, Winchester School of Art
    University of Helwan, Faculty of Applied Arts)

Abstract

This study proposes a novel assistive system architecture utilising Vuzix Blade 2 smart glasses to enhance the mobility and autonomy of visually impaired individuals. It addresses the limitations of current technologies by integrating real-time object detection, distance estimation, and optical character recognition (OCR), supported by auditory feedback. A comparative analysis between YOLOv11 and YOLOv12 models evaluates detection accuracy and computational efficiency, optimising system performance for wearable deployment. The system framework combines augmented reality, computer vision, and artificial intelligence (AI) to deliver context-aware support in dynamic environments. Evaluation results demonstrate the system’s capability to detect nearby obstacles within one metre, estimate distances accurately, and convert printed text into speech in real time. This work contributes a scalable and inclusive solution, offering practical insights for the development of intelligent assistive technologies. The findings underscore the potential of AI-enhanced wearable systems in advancing accessibility and promoting independent navigation for users with visual impairments.

Suggested Citation

  • Priyanka Kumari & Ramy Hammady, 2026. "A Novel AI-Driven Assistive Framework for Enhanced Navigation and Text Recognition for Visually Impaired Using Smart Glasses," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-11983-4_35
    DOI: 10.1007/978-3-032-11983-4_35
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:prbchp:978-3-032-11983-4_35. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.