Real-Time Ethiopian Currency Recognition for Visually Disabled Peoples Using Convolutional Neural Network

Abstract

Abstract A survey report made by the Ethiopian Ministry of Health along with several non-governmental organizations in 2006 G.C, there were about 5.3% of the Ethiopian population lives with blindness and low vision problems. This research work aims to develop a Convolutional Neural Network-based model by using pre-trained models to enable vision-impaired peoples to recognize Ethiopian currency banknotes in real-time scenarios. The models attempt to accurately recognize Ethiopian currency banknotes even if the input images come up with partially or highly distorted and folded Birr notes. 8500 (1700 for each class) banknotes data are collected within real-life situations by using 9 blind persons. The models were evaluated with 500 real-time videos of different conditions. The whole training, classification, and detection tasks have been demonstrated by adopting Tensorflow Object Detection API and the pre-trained Faster R-CNN Inception, and SSD MobileNet models. All the codes are implemented using Python. The model tested using numerous Ethiopian currencies at different banknotes status and light conditions. In the case of Faster R-CNN Inception model an average accuracy, precision, recall, and F1-score of 91.8%, 91.8%, 92.8%, and 91.8% are obtained respectively and in the case of SSD MobileNet model an average accuracy, precision, recall, and F1-score of 79.4%, 79.4%, 93.6%, and 84.4% are obtained respectively within a real-time video. Therefore as the first research work, the model has shown good performance in both models but Faster R-CNN provides a promising result with an average accuracy of 91.8%.