Comparative Study of Different Paper Currency and Coin Currency Recognition Method

has great importance in day to day life and may be because the currency recognition is a great area of interest for researchers. Different methods have been proposed by researchers for both coin and paper currency recognition. On the basic of vigorous literature survey, we can conclude that image processing is the most popular and effective method of currency recognition. Image processing based currency recognition technique consists of few basic steps like image acquisition, its pre-processing and finally recognition of the currency. Normally camera or scanner is used for image acquisition. Then these images are processed by using various techniques of image processing and various features are extracted from the images which are the key concept behind currency classification. In this paper, we have discussed various currency recognition methods proposed by different researchers and summarized their work.

[1]  Abdolah Chalechale Coin recognition using image abstraction and spiral decomposition , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[2]  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.

[3]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

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

[5]  A. Bastanfard,et al.  Sasanian coins classification using discrete cosine transform , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[6]  P.A.Vijaya A Robust Side Invariant Technique of Indian Paper Currency Recognition , 2012 .

[7]  Kazuyuki Murase,et al.  A Paper Currency Recognition System Using Negatively Correlated Neural Network Ensemble , 2010, J. Multim..

[8]  Hussein R. Al-Zoubi Efficient coin recognition using a statistical approach , 2010, 2010 IEEE International Conference on Electro/Information Technology.

[9]  Sigeru Omatu,et al.  A reliable method for recognition of paper currency by approach to local PCA , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[10]  Seema Bawa,et al.  Automated Coin Recognition System using ANN , 2013, ArXiv.

[11]  A. Rajaei,et al.  Feature Extraction of Currency Notes: An Approach Based on Wavelet Transform , 2012, 2012 Second International Conference on Advanced Computing & Communication Technologies.

[12]  Yasue Mitsukura,et al.  Design and evaluation of neural networks for coin recognition by using GA and SA , 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.

[13]  Hai-dong Wang,et al.  A paper currency number recognition based on fast Adaboost training algorithm , 2011, 2011 International Conference on Multimedia Technology.

[14]  Zhen Ji,et al.  Statictics of Gabor features for coin recognition , 2009, 2009 IEEE International Workshop on Imaging Systems and Techniques.

[15]  Laurens van der Maaten,et al.  COIN-O-MATIC: A fast system for reliable coin classification , 2006 .

[16]  Muhammad Sarfraz,et al.  Bahraini Paper Currency Recognition , 2012 .

[17]  D. A. K. S. Gunaratna,et al.  ANN Based Currency Recognition System using Compressed Gray Scale and Application for Sri Lankan Currency Notes - SLCRec , 2008 .