This paper examines the application of a deep learning approach to automatic coin recognition, via a mobile device and client-server architecture. We show that a convolutional neural network is effective for coin identification. During the training phase, we determine the optimum size of the training dataset necessary to achieve high classification accuracy with low variance. In addition, we propose a client-server architecture that enables a user to identify coins by photographing it with a smartphone. The image provided by the user is matched with the neural network on a remote server. A high correlation suggests that the image is a match. The application is a first step towards the automatic identification of coins and may help coin experts in their study of coins and reduce the associated expense of numismatic applications.

Implementation of a Coin Recognition System for Mobile Devices with Deep Learning

Capece, Nicola;Erra, Ugo;
2016-01-01

Abstract

This paper examines the application of a deep learning approach to automatic coin recognition, via a mobile device and client-server architecture. We show that a convolutional neural network is effective for coin identification. During the training phase, we determine the optimum size of the training dataset necessary to achieve high classification accuracy with low variance. In addition, we propose a client-server architecture that enables a user to identify coins by photographing it with a smartphone. The image provided by the user is matched with the neural network on a remote server. A high correlation suggests that the image is a match. The application is a first step towards the automatic identification of coins and may help coin experts in their study of coins and reduce the associated expense of numismatic applications.
2016
9781509056989
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/128301
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