An Intelligent Method for Indian Counterfeit Paper Currency Detection

Document Type: Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications


1 Department of ECE, Karunya Institute of Technology and Sciences, India.

2 Assistant Professor, Department of ECE, Karunya Institute of Technology and Sciences, India.


The production of counterfeit paper currencies has become cheaper because of the advancement in the printing technologies. The circulation of counterfeit currencies down the economy of a country. By leveraging this, there is a mandate to develop an intelligent technique for the detection and classification of counterfeit currencies. The intelligent techniques play a major role in the field of Human Computer Interaction (HCI) too. This paper deals with the detection of counterfeit Indian currencies. The proposed method feature extraction is based on the characteristics of Indian paper currencies. The first order and second order statistical features are extracted initially from the input. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8%. The experimental results are compared with state-of-the methods and produced reliable results.


Ahmadi & Omatu, S. (2003) A Methodology to Evaluate and Improve Reliabilty in Paper Currency Neuro-Classifiers. IEEE International Symposium on Computational Intelligence in Robotics and Automation 2003, Japan
Andrushia, A.D., & Thangarajan, R. (2015) Visual attention-based leukocyte image segmentation using extreme learning machine. Int J Adv Intell Paradig 7(2),172–186
Andrushia, A.D., & Thangarajan, R. (2019) RTS-ELM: an approach for saliency-directed image segmentation with ripplet transform. Pattern Anal Appl. https :// 4-019-00800 -8
Bhattacharjee, S.D., Yuan J., Jiaqi, Z., & Tan Y.P (2017) Context-aware graph-based analysis for detecting anomalous activities, ICME 2017, IEEE, pp. 1021–1026
Bhavani, R., & Karthikeyan, A. (2014) A novel method for counterfeit banknote detection. Int. J. Comput. Sci. Eng. 2, 165–167.
Bhavsar. K., Jani K., & Vanzara R. (2020) Indian Currency Recognition from Live Video Using Deep Learning. In: Chaubey N., Parikh S., Amin K. (eds) Computing Science, Communication and Security. COMS2 2020. Communications in Computer and Information Science, vol 1235. Springer, Singapore.
Debnath, K.K., Ahdikary, J.K., & Shahjahan, M. (2009) A currency recognition system using negatively correlated neural network ensemble, 12th International Conference on Computers and Information Technology, pp. 367–372.
Euisun Choi., Jongseok Lee., & Joonhym Yoon. (2006) “Feature Extraction for Bank Note Classification Using Wavelet Transform” IEEE International conference on Pattern recognition, China; DOI: 10.1109/ICPR.2006.553
Hasanuzzaman, F.M., Yang X., and Tian Y (2011) Robust and effective component-based bank note recognition by SURF features, 20th Annual Wireless and Optical Communications Conference, pp. 1–6.
Huber-Mörk R., Heiss-Czedik D., Mayer K., Penz, H., & Vrabl, A., (2007) Print process separation using interest regions. Lect. Notes Comput. Sci. 4673, 514–521.
Ji Woo Lee., Hyung Gil Hong., Ki Wan Kim & Kang Ryoung Park. (2017) A Survey on Banknote Recognition Methods by Various Sensors, Sensors, 17, 313; doi:10.3390/s17020313
Kang, K., & Lee, C., (2016) Fake banknote detection using multispectral images, in: 7th International Conference on Information, Intelligence, Systems & Applications, IISA, pp. 1–3.
Khin Nyein Nyein Hlaing (2015) “First Order Statistics and GLCM Based Feature Extraction for Recognition of Myanmar Paper Currency” Proceedings of 31st The IIER International Conference, Bangkok, Thailand
Lim, H., & Murukeshan, V. (2017) Hyperspectral imaging of polymer banknotes for building and analysis of spectral library, Opt. Lasers Eng. 98, 168–175
Mahajan, S., & Rane, K.P.(2014) A survey on counterfeit paper currency recognition and detection. In Proceedings of the International Conference on Industrial Automation and Computing, Nagpur, India, pp. 54–61.
Mizra, R., & Nanda, V. (2012) Design and Implementation of Indian paper currency authentication system based on feature extraction by edge based segmentation using sobel operator. Int.J Eng. Res., Dev 3(2) 41-46
Pourhabibi, T., Kok-Leong Ong., Booi H. Kam., & Yee Ling Boo (2020) Fraud detection: A systematic literature review of graph-based anomaly detection approaches, Decision Support Systems 133, 113303,
Rashid, A., Prati A., & Cucchiara, R. (2013) On the design of Embedded Solutions to banknote recognition. Opt. Eng. 52(9), 093106-1-093106-12
Roy, A., Halder, B., Garain, U., & Doermann, D.S. (2015) Machine-assisted authentication of paper currency: An experiment on Indian banknotes. Int. J. Doc. Anal. Recognit. 18, 271–285
Rusanov, V., Chakarova, K..Winkler, H., Trautwein, A.X., & Mössbauer (2009) and X-ray fluorescence measurements of authentic and counterfeited banknote pigments. Dyes Pigments 81, 254–258.
Russ J.C., The Image Processing Handbook, 5th edition, CRC Press, Boca Raton, FL, USA, 2006.
Sangwook Baek, Euisun Choi, Yoonkil Baek, Chulhee Lee (2018) Detection of counterfeit banknotes using multispectral images Digital Signal Processing,78, Pages 294-304
Sargano, A., Sarfraz M,. & Haq, N., (2013) Robust features and paper currency recognition system. The 6th International Conference on Information Technology ICIT 2013, Amman, Jordan
Sarkar A., Verma R., & Gupta G. (2013) Detecting Counterfeit Currency and Identifying Its Source. In: Peterson G., Shenoi S. (eds) Advances in Digital Forensics IX. DigitalForensics 2013. IFIP Advances in Information and Communication Technology, vol 410. Springer, Berlin, Heidelberg
Sharma, B., Kaur, A., & Vipan, (2012) Recognition of Indian Paper Currency based on LBP. Int.J.Comput.Appl. 59(1), 24-27
Sun, B., & Li J (2008) Recognition for the banknotes grade based on CPN. In Proceedings of the Computer Science and Software Engineering (CSSE 2008), Hubei, China, pp. 90–93.
Takeda, F., and Nishikage, T., (2000) Multiple kinds of paper currency recognition using neural network and application for Euro currency, Joint Conference on Neural Networks 2, 143–147.
Takeda, F., Sakoobunthu, L., & Satou, H (2003) Thai bank note recognition using neural network and continues learning by DSP unit, Knowledge based Intelligent Information and Engineering System, (2003), pp. 1169–1177.
Vanessada Silva Oliveira, Ricardo Saldanha Honorato, Fernanda AraújoHonorato, Claudete Fernandes Pereir (2018) Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics, Forensic Science International, 286, 121-127
West, J., & Bhattacharya, M. (2016) Intelligent financial fraud detection: a comprehensive review, Computers & Security 57, 47–66,
Zhang, Q., Yan,W.Q., & Kankanhalli,M (2019) Overview of currency recognition using deep learning. J. Banking Financ. Technol. 3(1), 59–69.