• 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.

• 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.

• 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.