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Sensors 2017, 17(7), 1595; https://doi.org/10.3390/s17071595

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

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
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Received: 9 June 2017 / Revised: 6 July 2017 / Accepted: 6 July 2017 / Published: 8 July 2017
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: multi-national banknote classification; visible-light banknote images; one-dimensional line sensor; convolutional neural network multi-national banknote classification; visible-light banknote images; one-dimensional line sensor; convolutional neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Pham, T.D.; Lee, D.E.; Park, K.R. Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network. Sensors 2017, 17, 1595.

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