Object Identification Using Mobile Device for Visually Impaired Person

Date of Award


Degree Name

M.S. in Computer Science


Department of Computer Science


Mehdi R. Zargham


The human eye perceives up to 80% of all the impressions and acts as the best shield from threat. While it is believed and accepted that vision is a predominant sense in people, as per the World Health Organization, around 40 million individuals on the planet are blind, and 250 million have some type of visual disability. As a result, a lot of research and papers are being suggested to create accurate and efficient navigation models utilizing computer vision and deep learning approaches. These models should be fast and efficient, and they should be able to run on low-power mobile devices to provide real-time outdoor assistance. Our objective is to extract and categorize the information from the live stream and provide audio feedback to the user within the University campus. The classification of the objects in the stream is done by a CNN model and sent as an input for the voice feedback, which is divided into several frames using the OpenCV library and converted to audio information for the user in the real-time environment using the Google text to speech module. The results generated by the CNN model for image classification have an accuracy of over 95 percent, and real-time audio conversion is a rapid transition technique, resulting in an algorithm that performs competing with other prior state-of-art methods. We also want to integrate the application in smartphones, into our mobile app to provide a more user-friendly experience for the end-users.


Computer Science, Classification, CNN (Convolutional Neural Network), Visually Impaired, Voice Alert, Mobile Device, University Premises

Rights Statement

Copyright 2021, author