International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 8 - Issue 8, August 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Implementation Of Template Matching, Fuzzy Logic And K Nearest Neighbor Classifier On Philippine Banknote Recognition System

[Full Text]



Rodel Emille T. Bae, Edwin R. Arboleda, Adonis Andilab, Rhowel M. Dellosa



Currency Recognition System, Fuzzification, Fuzzy Logic, Image Processing, KNN, Inference Method, MatLab, Mamdami,



Currency recognition system is one of the most marked research topics at present. Lots of variety of applications triggered the researchers for a study like this. Monetary transaction is natural in human being for its daily transactions. The need to use an artificial intelligence to come up with determination and classifications of banknote may contribute to the improvement of artificial intelligence applications. An attempt was made in this study to apply image processing, fuzzy logic and K Nearest Neighbor for the improvement on the limitations of the existing currency recognition systems. The study has is divided into two parts: a template matching technique for feature extraction and comparison of accuracy between fuzzy logic algorithm and KNN based on the information gathered by the first part. Thus by implementing the Image Processing and FLC in the MATLAB with the help of MATLAB programming and fuzzy logic toolbox, recognition of Philippine currency will be more accurate. KNN shows it flaws to identify the features resulting to big errors unlike the first method.



[1] Velu C and Vivekanandam P, “Indian Coin Recognition System of Image Segmentation by Heuristic Approach and Houch Transform (HT) Int. J. Open Problems Compt. Math., Vol. 2, No. 2, June 2009
[2] Debnath, K., Ahmed, S and Shahjahan M, 2010. A paper currency recognition system using negatively correlated neural network ensemble. J. Multimedia, 5(6): 560-567.
[3] Jahangir, N. and Raja A, 2007. Bangladeshi banknote recognition by neural network with axis symmetrical masks. Proceeding of the 10th International Conference on Computer and Information Technology, pp: 105.
[4] Guo, J., Zhao Y and Cai A, 2010. A reliable method for paper currency recognition based on LBP. Proceeding of the 2nd IEEE International Conference on Network Infrastructure and Digital Content, pp: 359-363.
[5] Lee, J. and Kim H, 2003. New recognition algorithm for various kinds of Euro banknotes. Proceeding of the IEEE 29th Annual Conference of Industrial Electronics Society, pp: 2266-2270.
[6] Agarwal, H and Kumar P,”Indian currency note Denomination recognition using color images”, International Journal of Computer Engineering and Communication Technology, ISSN 2278-5140, Vol 1, Issue 1.
[7] Pawade D, Chaudhari P and Sonkamble H, “Comparitive Study of Different Paper currency and coin currency Recognition Method”, International Journal of Computer Application, ISSN 0975-8887, Vol 66, No 23, Mar 2013.
[8] Qing, B. and Xun J, 2010. Currency recognition modeling research based on BP neural network improved by gene algorithm. Proceeding of the 2nd International Conference on Computer Modeling and Simulation, pp: 246250.
[9] Arboleda, E. R., Fajardo, A. C., & Medina, R. P. (2018b). An Image Processing Technique for Coffee Black Beans Identification. In 2018 IEEE International Conference on Innovative Research and Development (ICIRD) (pp. 1–5). IEEE.
[10] Singh, P., G. Krishan and S. Kotwal, 2011. Image processing based heuristic analysis for enhanced currency recognition. Int. J. Adv. Technol., 2(1): 82-89.
[11] Arboleda, E. R. (2018). Discrimination of civet coffee using near infrared spectroscopy and artificial neural network. International Journal of Advanced Computer Research, 8(39), 324–334.
[12] L. A. Zadeh, "Fuzzy sets", Inform. Contr., vol. 8, pp. 338-353, 1965.
[13] Arboleda, E. R., Fajardo, A. C., & Medina, R. P. (2018a). Classification of Coffee Bean Species Using Image Processing , Artificial Neural Network and K Nearest Neighbors. 2018 IEEE International Conference on Innovative Research and Development (ICIRD), (May), 1–5.
[14] Arboleda, E. R. (2019). Comparing Performances of Data Mining Algorithms for Classification of Green Coffee Beans. International Journal of Engineering and Advanced Technology (IJEAT), 8(5), 1563–1567.
[15] Dellosa R, Fajardo A and Medina R. “A Heuristic Approach of Location Estimation Based on Pre-defined Coordinates” IJRTE, Vol 7, Issue 6, pp 1110-1113, March 2019