Deep Learning Approach for Ethiopian Banknote Denomination Classification and Fake Detection System
Asfaw Alene Shefraw Alene Shefraw, Bahir Dar Institute of Fashion and Textile Technology, Bahir University, BahirDar, Ethiopia.
Currently, in banks and the financial institution in Ethiopia, it is most commonly observed to use self-serving devices to outreach their services and also to make payment through for their ordinary customers. But they are unable to fully utilizing their self-serving devices to its fullest capacity. As a result of the automatic software-based fake banknote recognition system, it causes a number of counterfeit banknotes to be flooded in the market. Nevertheless, banks have not yet utilized a reliable recognition system to identify forged banknotes. This calls for the development of a better authenticity verification system. In this study, we have examined the convolutional neural network as a feature extraction technique and feed-forward artificial neural network as a classifier to design the Ethiopian banknote recognition system. This article presents a robust and efficient system for Ethiopian banknote recognition. A high banknote recognition and classification rate were achieved using FFANN as a classifier convolutional neural network as a classifier with real scene images taken from the scanner. From the experimental results, the study observed an average accuracy of 99.4% for classifying the banknote denomination and 96% accuracy for fake currency detection. We, therefore, recommend that a further investigation on the CNN model using advanced architecture like Google Net and ResNet with the larger dataset to study the banknote classification and verification system.
Convolutional Neural Network, Ethiopian Banknote Classification System, Ethiopian Paper Currency Recognition System
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