Computer Science & Electrical
Received: 02 Jan 2019 , Published: 09 January 2019
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The ubiquitous method of counting money by hand is definitely not acceptable when large amount money is to be dealt with, from time and accuracy stand points, which have led to the use of Automated Money Counter to determine the number of fed notes without distinguishing their denominations. Although numerous algorithms and devices have been developed to detect denominations for several currencies but, based on all accessible literatures, this paper seems to be the first attempt to automatically detect Nigeria currency denominations as it provides an algorithm that detects both paper-based and polymer-based Nigeria currencies. Thus, this paper set the pace for future research studies in the area of automatic Nigerian Naira denominations recognition systems. The algorithm used is achievable with relatively cheap hardware requirements, yet with substantial overall accuracy of 98.75% for non-mutilated notes but it is yet to be tested with defaced and mutilated notes. The algorithm developed can be deployed to function as mixed-denominations money counter or as denomination detector.
Almu A., and Muhammad B.A., 2017, Image-Based Processing of Naira Currency Recognition, Annals. Computer Science Series 15, p. 169-173 Shettigar A., and Singal P., 2017, Forex Detection Using Neural Networks in Image Processing, International Journal of Engineering Science and Computing 7(5), p. 11851- 11854 Sarfraz M., 2015, An Intelligent Paper Currency Recognition System, Procedia Computer Science 65, P.538-545 Althafiri B., Sarfraz M., and Alfarras M., 2012, Bahraini Paper Currency Recognition, Journal of Advanced Computer Science and Technology Research 2(2), p. 104-115 Bhawani S., Amandeep K., and Vipan, 2012, Recognition of Indian Paper Currency Based LBP, International Journal of Computer Application 59(1), 2012, p. 24-27