A High Performance Banknote Recognition System Based on a One-Dimensional Visible Light Line Sensor

An algorithm for recognizing banknotes is required in many fields, such as banknote-counting machines and automatic teller machines (ATM). Due to the size and cost limitations of banknote-counting machines and ATMs, the banknote image is usually captured by a one-dimensional (line) sensor instead of a conventional two-dimensional (area) sensor. Because the banknote image is captured by the line sensor while it is moved at fast speed through the rollers inside the banknote-counting machine or ATM, misalignment, geometric distortion, and non-uniform illumination of the captured images frequently occur, which degrades the banknote recognition accuracy. To overcome these problems, we propose a new method for recognizing banknotes. The experimental results using two-fold cross-validation for 61,240 United States dollar (USD) images show that the pre-classification error rate is 0%, and the average error rate for the final recognition of the USD banknotes is 0.114%.

[1]  F. P. Ahangaryan,et al.  Persian Banknote Recognition Using Wavelet and Neural Network , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

[2]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Sigeru Omatu,et al.  A Neuro-Money Recognition Using Optimised Masks by GA , 1994, IEEE/Nagoya-University World Wisepersons Workshop.

[5]  Sigeru Omatu,et al.  Classification of the Italian Lira using the LVQ method , 1999, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[6]  Michifumi Yoshioka,et al.  Reliable Banknote Classification Using Neural Networks , 2009, 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences.

[7]  Shie-Jue Lee,et al.  Employing multiple-kernel support vector machines for counterfeit banknote recognition , 2011, Appl. Soft Comput..

[8]  Jongseok Lee,et al.  Feature Extraction for Bank Note Classification Using Wavelet Transform , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[9]  Guowei Yang,et al.  Employing quaternion wavelet transform for banknote classification , 2013, Neurocomputing.

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

[11]  Giovanni Maria Farinella,et al.  Forgery Detection and Value Identification of Euro Banknotes , 2013, Sensors.

[12]  Giovanni Maria Farinella,et al.  Counterfeit Detection and Value Recognition of Euro Banknotes , 2013, VISAPP.

[13]  Xiaodong Yang,et al.  Robust and effective component-based banknote recognition by SURF features , 2011, 2011 20th Annual Wireless and Optical Communications Conference (WOCC).

[14]  Farid García,et al.  Recognition of Mexican banknotes via their color and texture features , 2012, Expert Syst. Appl..

[15]  Michifumi Yoshioka,et al.  Bank note classification using neural networks , 2007, 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007).

[16]  Ali Ahmadi,et al.  A study on evaluating and improving the reliability of bank note neuro-classifiers , 2003, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[17]  Zongmin Ma,et al.  A Banknote Orientation Recognition Method with BP Network , 2009, 2009 WRI Global Congress on Intelligent Systems.

[18]  Sigeru Omatu,et al.  A PCA based method for improving the reliability of bank note classifier machines , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[19]  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).

[20]  Aoba Masato,et al.  Euro Banknote Recognition System Using a Three - layered Perceptron and RBF Networks , 2003 .

[21]  Ali Ahmadi,et al.  Improvement of reliability in banknote classification using reject option and local PCA , 2004, Inf. Sci..

[22]  Hiroshi Sako,et al.  A Hierarchical Classification Method for US Bank Notes , 2005, MVA.

[23]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.