YCL Based Smart Glasses for Visually Impaired

Citation

Wong, Wai Kit and Loh, Yao Wei and Lee, Wei Xiang and Venkatasamy, Thiruppathy Kesavan and Min, Thu Soe @ M Sait and Wong, Eng Kiong (2026) YCL Based Smart Glasses for Visually Impaired. Journal of Engineering Technology and Applied Physics, 8 (1). p. 133. ISSN 2682-8383

[img] Text
View of YCL Based Smart Glasses for Visually Impaired.pdf - Published Version
Restricted to Repository staff only

Download (7MB)

Abstract

A key component of assistive technology that helps people with visual impairments access text in their daily lives is a text reader. An image-based text recognition tool for the blind is presented in this article. It takes pictures of the user's surroundings using a dual-camera module. For improving character detection and recognition in thesedeveloped smart glasses for the blind and the visually impaired, a hybrid algorithm called the YCL Character Recognition Algorithm thatcombines You Only Look Once (YOLO), Convolutional Recurrent Neural Networks (CRNN), and Long Short-Term Memory (LSTM) to balance out the drawbacks of each algorithm by learning from its advantages.The suggested YOLO-v8model is used for real-time text object detection, CRNN is used toextract character features, and LSTM is used to enhance sequential character prediction.An audio output signal is given to the user by the image processing software after the visual data has been processed. Thesesmart glasseshavethe benefit of being able to view characters from both close and far distances. A specially acquired dataset was used to evaluate the suggested YCL technique, which shows notable speed and accuracy gains over the traditional homogenous algorithms. The suggested method is successful in identifying words and characters and in delivering audio output for the blind and visually impaired, based on users’surveys and experimental data.

Item Type: Article
Uncontrolled Keywords: Machine learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 09 Jul 2026 04:17
Last Modified: 09 Jul 2026 04:17
URII: http://shdl.mmu.edu.my/id/eprint/16357

Downloads

Downloads per month over past year

View ItemEdit (login required)