• Journal of Information Processing Systems
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• v.16 no.1
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.364-370
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• 2002

A banknote counter is a machine that automates counting the money in some agencies to treat much banknotes as well as general banking agencies. The banknote counter materialized in this paper is the machine that adds the function of banknote sorting, detecting plural banknote and detecting counterfeit banknote to an existing banknote counter. The technique of sensor signal processing are used for banknote sorting. The technique of sensor application and data processing are used for detecting counterfeit banknote. The technique of precision equipment design and microprocessor application are used for high speed count. Software improved in debugging and difficulties to link with additional hardware. It was materialized through effective control algorithm and real-time signal processing with C-language on the basis of RTOS(real-time operating system) Photodiode, its applications and a magnetic resistance sensor are used as a sensor device with regard to hardware cost -cutting and process velocity. PCF80C552-24 of Philips using Intel I8051 core is used as a control microprocessor. As the results so far achieved, counterfeit banknotes made by the use of a color duplicator and a color Printer, are distinguished from real banknotes through mixing an optical with a magnetic sensor. and, in case that there are some different banknotes while counting, it is prevented for them to be counted without discriminating from the same kind of banknotes in addition to the fu notion of banknote sorting.

• Design & Manufacturing
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• v.11 no.1
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• pp.19-25
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• 2017

A conventional taped banknotes detecting device in the banknote counting machine have some problems caused by temperature, humidity, dust and other environmental conditions. In order to the improve the detection performance of taped banknote in the banknote counting machine, we developed taped banknotes detecting device that the ultrasonic wave sensor was replaced by mechanical method device. There are many factors for the performance of the taped banknote detecting device of a mechanical method, we considered three main factors that are the amplification ratio of the thickness of banknotes, the pressing force of banknotes and the feeding speed of banknotes. We carried out the experiments with the 27 kinds of combination of these three main factors. As the result, accurate detection was possible to the normal banknotes and taped banknotes regardless of the feeding rate in terms of the amplification rate 27 times and the pressing force 660gf.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.834-835
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• 2014

Determination of banknote fitness is an important and challenging task, which classifies used banknotes whether are suitable for recirculation or should be replaced by new ones. Many researches on solving this problem have been conducted on various types of banknote, based on using optical sensors and discrimination algorithms. In this study, we give a review on the previous works on banknote fitness classification.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.67-72
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• 2019

This paper proposes a computer vision-based banknote recognition system intended to assist the blind. This system is robust and fast in recognizing banknotes on videos recorded with a smartphone on real-life scenarios. To reduce the computation time and enable a robust recognition in cluttered environments, this study segments the banknote candidate area from the background utilizing a technique called Pixel-Based Adaptive Segmenter (PBAS). The Speeded-Up Robust Features (SURF) interest point detector is used, and SURF feature vectors are computed only when sufficient interest points are found. The proposed algorithm achieves a recognition accuracy of 98%, a 100% true recognition rate and a 0% false recognition rate. Although Korean banknotes are used as a working example, the proposed system can be applied to recognize other countries' banknotes.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.685-692
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• 2016

In this paper, we not only take advantage of the SIFT features in banknote recognition, which has robustness to illumination changes, geometric rotation as well as scale changes, but also propose the hierarchical banknote recognition algorithm, which comprised of feature vector extraction from the frame grabbed image of the banknotes, and matching to the prepared data base of multinational banknotes by ANN algorithm. The images of banknote under the developed UV, IR and white illumination are used so as to extract the SIFT features peculiar to each banknotes. These SIFT features are used in recognition of the nationality as well as face value. We confirmed successful function of the proposed algorithm by applying the proposed algorithm to the banknotes of Korean and USD as well as EURO.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.5
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• pp.141-146
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• 2007

Barcodes have been widely used to implement automatic identification systems but there are various problems such as security weakness or distance restriction in scanning barcode signals in a barcode-based automatic identifcation systems. Recently researchers are gradually interested in radio frequency identification (RFID) and RFID systems have been applied to various fields than before. Especially one of RFID application fields, a bank system uses RFID tagged bankontes to prevent illegal transactions such as counterfeiting banknotes and money laundering. In this paper, we propose a RFID system for protecting location provacy of a banknote holder. In addition, our paper describes that a trust party can trace a counterfeit banknote holder to provide against emergencies.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.848-852
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• 2001

A new approach to the design of bank note counters is suggested. Modern bank note counters are equipped with several types of sensors for counting and detection of counterfeit bank notes. To achieve higher speed of counting while maintaining the sensing capability, longitudinal feeding scheme is proposed. Several merits of longitudinal feeding are discussed. Taguchi method is used for the optimization of the feeding mechanism.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.201-206
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• 2004

Counters for the various kinds of banknotes require high-speed distinctive point extraction and recognition. In this paper we propose a new point extraction and recognition algorithm for Euro banknotes. For distinctive point extraction we use a coordinate data extraction method from specific parts of a banknote representing the same color. To recognize banknotes, we trained 5 neural networks. One is used for inserting direction and the others are used for face value. The algorithm is designed to minimize recognition time by using a minimal amount of recognition data. The simulated results show a high recognition rate and a low training period. The proposed method can be applied to high speed banknote counting machines.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1149-1160
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• 2011

Model selection and hypothesis testing problems in Bayesian inference are still debated between scholars. Bayesian factors traditionally used as a criterion in Bayesian hypothesis testing and model selection, are easy to understand but sometimes hard to compute. In addition, there are other model selection criterions such as DIC(Deviance Information Criterion) by Spiegelhalter et al. (2002) and Bayesian P-values for testing. In this paper, we briefly introduce the Bayesian hypothesis testing and model selection procedure. In addition we have applied a Bayesian inference to Swiss banknote data by a fitting logistic regression model and computing several test statistics to see if they provide consistent results.