Principal features for Indian currency recognition

Currency recognition system is one of the fast growing research fields under image processing. This paper proposes a novel method for Indian currency recognition. Our proposed approach identifies denomination by extracting features like Center Numeral, Shape, RBI Seal, Latent Image and Micro Letter. Principal Component Analysis is used to reduce the dimensions and a similarity based classifier is constructed to predict test sample. Results are also validated by constructing models using classifier implemented using WEKA and testing with unseen samples not considered in feature extraction. Our study demonstrated that center numeral results in an accuracy of 100% with all family of currencies.

[1]  Fumiaki Takeda,et al.  Multiple kinds of paper currency recognition using neural network and application for Euro currency , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  Marco Gori,et al.  A neural network-based model for paper currency recognition and verification , 1996, IEEE Trans. Neural Networks.

[3]  Hamid Hassanpour,et al.  Using Hidden Markov Models for paper currency recognition , 2009, Expert Syst. Appl..

[4]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[5]  Bu-Qing Cao,et al.  Currency Recognition Modeling Research Based on BP Neural Network Improved by Gene Algorithm , 2010, 2010 Second International Conference on Computer Modeling and Simulation.

[6]  Anna Vilà,et al.  Development of a fast and non-destructive procedure for characterizing and distinguishing original and fake euro notes , 2006 .

[7]  Hamid Hassanpour,et al.  Feature extraction for paper currency recognition , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[8]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[9]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[10]  Xiaodong Yang,et al.  Robust and Effective Component-Based Banknote Recognition for the Blind , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Sung Bum Pan,et al.  A Study on the Korean Banknote Recognition Using RGB and UV Information , 2009, FGIT-FGCN.

[12]  Lindsay I. Smith,et al.  A tutorial on Principal Components Analysis , 2002 .

[13]  Anni Cai,et al.  A reliable method for paper currency recognition based on LBP , 2010, 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content.

[14]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[15]  Bo Jiang,et al.  Research on paper currency recognition by neural networks , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[16]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[17]  Felipe Grijalva,et al.  Smartphone recognition of the U.S. banknotes' denomination, for visually impaired people , 2010, 2010 IEEE ANDESCON.

[18]  Jun Ho Oh,et al.  High Speed Paper Currency Recognition by Neural Networks , 1997 .

[19]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[21]  Bhupesh Kumar Singh,et al.  Indian currency recognition based on texture analysis , 2011, 2011 Nirma University International Conference on Engineering.