Object Recognition and Assistance System for Visually Impaired Shoppers

Abstract

Shopping is indeed effortless for many individuals. However, it could certainly be a struggle and chaotic experience for the visually impaired. Visual impairment causes many societal stigma and inconvenience to visually impaired individuals. Although shopping may sound extremely easy, this is a crucial social activity for many visually impaired (VI) individuals. Visually impaired (VI) shoppers always require assistance when shopping for product identification purposes. This may lead to greater inconvenience as delays, lack of information and product familiarity of shop assistants may occur. Therefore, allowing visually impaired shoppers to independently perform shopping regardless of size and position of the shopping mall is essential. This encourages them to participate in enhanced social activities and perform their daily chores in independence. Although many products have been developed to assist visually impaired shoppers at shopping malls, due to their drawbacks, some of these have seem to undergo failures in producing accurate information to the visually impaired shopper for object identification and caused inconvenience. This project proposes a feasible solution for visually impaired shoppers to perform their shopping at ease and independently. Object recognition has been made possible in order to identify garment items while shopping with no assistance of another individual. The Convolutional Neural Network (CNN) has been used to obtain a sufficiently good accuracy and precision with a validation accuracy of 90%. Some of the novel techniques such as Ensemble Modelling has also been performed in order to reduce any generalization errors of the prediction and achieve a greater accuracy while overcoming all of the drawbacks of the currently existing products in the market. The overall product is proposed to attain maximum consumer population of visually impaired shoppers with satisfaction, reliability, and low cost.