Development of a Neural Network Model for Currency Detection to aid visually impaired people in Nigeria

TL;DR

This paper presents a neural network-based system that accurately identifies Nigerian currency to assist visually impaired individuals, improving transaction ease and independence.

Contribution

It introduces a custom dataset and a neural network model specifically designed for Nigerian cash recognition, advancing assistive technology for visually impaired users.

Findings

Mean Average Precision over 90%

Effective currency differentiation for visually impaired

Potential to enhance independence in transactions

Abstract

Neural networks in assistive technology for visually impaired leverage artificial intelligence's capacity to recognize patterns in complex data. They are used for converting visual data into auditory or tactile representations, helping the visually impaired understand their surroundings. The primary aim of this research is to explore the potential of artificial neural networks to facilitate the differentiation of various forms of cash for individuals with visual impairments. In this study, we built a custom dataset of 3,468 images, which was subsequently used to train an SSD neural network model. The proposed system can accurately identify Nigerian cash, thereby streamlining commercial transactions. The performance of the system in terms of accuracy was assessed, and the Mean Average Precision score was over 90%. We believe that our system has the potential to make a substantial contribution to the field of assistive technology while also improving the quality of life of visually challenged persons in Nigeria and beyond.