Fake Indian Paper Currency Detection
A NOVEL APPROACH OF EMBEDDED SYSTEM FOR INDIAN PAPER CURRENCY RECOGNITION
Abstract-This paper presents the Embedded System for detection of the counterfeit Indian Paper Currency. The proposed system works with all the types of denominations of Indian paper currency. This system relies on a specific feature of the Indian Bank Notes. The relied feature is not possible to replicate for the counterfeit makers or producers. And there is no foreseeable likelihood that they would be capable to imitate this feature even within a pretty long time. The recognition system is composed of three parts. The captured image is first preprocessed by reducing data dimensionalities and extracting its features by using image processing toolbox in MATLAB. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished. The second one is recognition in which the core is neural network classifier. And Finally the result of recognition will be displayed on AVR microcontroller ATMega32. The microcontroller then determines the validity of the note by glowing LED for Counterfeit Paper Currency.
I. INTRODUCTION
The image processing involves changing the nature of an image in order to improve its pictorial information for human interpretation. The image processing toolbox software is a collection of functions that extend the capability of the MATLAB numeric computing environment. The toolbox supports a wide range of image processing operations on the given image. Scientists and engineers have developed various approaches to deal with such recognition problems. We have analyzed the properties of the HSV (Hue, Saturation and Value)color space with emphasis on the visual perception of the variation in Hue, Saturation and Intensity values of an image pixel. The HSV color space is fundamentally different from the widely known RGB color space since it separates out the Intensity (luminance) from the
Abstract-This paper presents the Embedded System for detection of the counterfeit Indian Paper Currency. The proposed system works with all the types of denominations of Indian paper currency. This system relies on a specific feature of the Indian Bank Notes. The relied feature is not possible to replicate for the counterfeit makers or producers. And there is no foreseeable likelihood that they would be capable to imitate this feature even within a pretty long time. The recognition system is composed of three parts. The captured image is first preprocessed by reducing data dimensionalities and extracting its features by using image processing toolbox in MATLAB. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is finished. The second one is recognition in which the core is neural network classifier. And Finally the result of recognition will be displayed on AVR microcontroller ATMega32. The microcontroller then determines the validity of the note by glowing LED for Counterfeit Paper Currency.
I. INTRODUCTION
The image processing involves changing the nature of an image in order to improve its pictorial information for human interpretation. The image processing toolbox software is a collection of functions that extend the capability of the MATLAB numeric computing environment. The toolbox supports a wide range of image processing operations on the given image. Scientists and engineers have developed various approaches to deal with such recognition problems. We have analyzed the properties of the HSV (Hue, Saturation and Value)color space with emphasis on the visual perception of the variation in Hue, Saturation and Intensity values of an image pixel. The HSV color space is fundamentally different from the widely known RGB color space since it separates out the Intensity (luminance) from the
References: [1] Ali Ahmadi, Sigeru Omatu and Toshihisa Kosaka,” A Reliable Method for Recognition of Paper Currency by Approach to Local PCA”, [2] D. A. K. S. Gunaratna, N. D. Kodikara, H.L.Premaratne,”ANN Based currency Recognition System using Compressed Gray Scale and Application for Sri Lankan currency Notes-SLCRec”, World Academy of Science,Engineering and Tehnology,2008 [3] F. Takcda and S. OmaN, “Bank Note Recognition System Using neural Network with Random Masks,” Proceeding of the World Congress an Neural Networks, Vol. I , pp. 241-244, Portland, USA, 1993. [4] M. Teranishi, S. Omahl and T. Kosaka, “Neura-classifier ofcurrency Fatigue Level Based on Acoustic Cepstlum Pattems,” Joumal of Advanced Computational Intelligence, Vol. 4, No. I, pp. 18-23, 2000.