This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. For character extraction, we report an overlap-recall rate of 79.68% and an overlap-precision rate of 98.10% respectively.ĭespite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. The experiments demonstrate that the proposed binarization method outperforms other well-known methods. After that, a local contrast average method is introduced to extract the RMB characters from the binarization result. Then the detected text region is binarized by a combined thresholding technique. First, two different techniques, namely skew correction and orientation identification are used to detect the region which contains RMB serial number. In this paper, we present a new system that extracts the RMB characters directly from scanned RMB images. The accuracy of RMB recognition relies heavily on the extraction, which is a challenging problem due to background variations and uneven illumination. The study of RMB (renminbi bank note, the paper currency used in China) serial number recognition draws more and more attention in recent years, for reducing financial crime, improving financial market stability and social security.
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