Abstract
In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods.
Highlights
The functionalities of sorting and classifying paper currency in automated transaction facilities, such as automated teller machines (ATMs) or counting machines consist of the recognition of banknote types, denominations, counterfeit detection, serial recognition, and fitness classification [1]
We considered a method for classification of banknote fitness based on the convolutional neural network (CNN)
On the evaluation of a state-of-the-art method, we proposed a deep learning-based banknote fitness-classification method using a CNN on the gray-scale banknote images captured by visible-light one-dimensional line image sensor
Summary
The functionalities of sorting and classifying paper currency in automated transaction facilities, such as automated teller machines (ATMs) or counting machines consist of the recognition of banknote types, denominations, counterfeit detection, serial recognition, and fitness classification [1]. The fitness classification of banknotes is concerned with the evaluation of the banknotes’ physical conditions, such as staining, tearing, or bleaching. This task helps to determine whether a banknote is suitable for recirculation or should be replaced by a new one, and to enhance the processing speed and sorting accuracy of the counting system. We provide detailed explanations of the related work concerning banknote fitness classification
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