Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network

Tuyen Pham,Kang Park,Dong Lee

doi:10.3390/s17071595

Abstract

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods.

Highlights

Automated transaction facilities, such as automated teller machines (ATMs) and multifunctional counting machines, should be able to process various tasks, such as denomination determining, fitness classification, and counterfeit detection, with currencies from various countries and regions [1]
Experiments using the proposed method were conducted on a multi-national banknote image database containing images from six national currencies: Chinese yuan (CNY), EUR, Japanese yen (JPY), Korean won (KRW), RUB, and United State dollar (USD)
We proposed a multi-national banknote recognition method based on the adoption adoption of convolutional neural network (CNN) for feature extraction and classification

Summary

Introduction

Automated transaction facilities, such as automated teller machines (ATMs) and multifunctional counting machines, should be able to process various tasks, such as denomination determining, fitness classification, and counterfeit detection, with currencies from various countries and regions [1]. One of the most popular approaches is based on an image processing method, which has been considered an effective solution [2,3]. In this method, a recognition system captures an input banknote by using visible light sensors and determines its denomination based on the classification of the input direction of banknote. In the method proposed by Pham et al [5], currency of the US dollar (USD), South African rand (ZR), Angolan kwanza (AOA), and Malawian kwacha (MWK) are recognized by a K-means-based classifier with the features extracted by principal component analysis (PCA) of the discriminative regions on banknote images.

Methods

Results

Conclusion