An Artificial Neural Networks Based Fake Currency Detection System
AUTHORS
Bosubabu Sambana,Department of Computer Science and Engineering, Viswanadha Institute of Technology and Management, Visakhapatnam, India
Mohan Mahanty,Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, India
ABSTRACT
We propose a technique for web access by infusing or embeddings ordering different nations notes. An Image is separating and preparing procedure to recognize and match the distinguished information required cash picture and the first reference picture, each money note taken a Region of Interest (ROI) on existing money note condition. A separated cash picture ROI can be utilized to different example development and acknowledgement procedures and ANN hubs recognizing systems. At once, numerous cash notes are distinguished by coordinated notes then a web seek based following framework to recognize coordinating procedure is allowed for getting to for their specified timeframe. At first, we secure required the cash note by average level picture scanner on settled dpi shading with a required size arrangement; the dpi pixels level is set to get an ordinary picture utilizing picture preparing strategy. Barely any cutting edge picture channels are connected to proposed picture remarkable estimation of required cash take note of, this relegated esteem or images are contrasted and the doled out info sign images to coordinate unique note esteem, at that point web-based getting to technique controls by the microcontroller to examine all prerequisite fields and fundamental activities.
KEYWORDS
Flat filters, Currency, Region of interest, Pattern recognition, Artificial intelligence, Neural networks, Currency recognition, Image processing, Grayscale images
REFERENCES
[1] Surjeet Singh and KulbirSingh, “Segmentation techniques for iris recognition system,” International Journal of Scientific & Engineering Research, vol.2, no.4, (2011)
[2] Chinmay Bhurke, Meghana Sirdeshmukh, M.S. Kanitkar, “Currency recognition using image processing,” International Journal of Innovative Research in Computer and Communication Engineering, vol.3, no.5, (2015)
[3] Zheng He, “Classification technique in pattern recognition; faculty of IT,” University of Technology, January 31 - February 04, (2009)
[4] SeemaAsht and Rajeshwar Dass, “Pattern recognition techniques: A review,” International Journal of Computer Science and Telecommunications, vol.3, no.8, (2012)
[5] Harish Agarwal and Padam Kumar, “Indian currency note denomination recognition in color image,” Int. Journal on Advanced Computer Eng. and Communication Tech, vol.1, (2012)
[6] Amel Saeed Tuam, “Iris image segmentation and recognition,” Int. J Comp Sci. Emerging Tech, vol.3 no.2 (2012)
[7] A. Ms. Trupti Pathrabe and B.Dr. N.G.Bawane, “Paper currency recognition system using characteristics extraction and negativity Correlated NN Ensemble,” Int. Journal of Latest Trends in Computing, (2010)
[8] Jain Mao, “Statistical pattern recognition: A review, Robert,” IEEE transaction on machine learning and pattern analysis, vol.22, no.1, (2000)
[9] D. A. KS. Gunaratna, N.D. Kodikara, and 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 Technology, (2008)
[10] Eun-Hoe Goo, “Assessment of image quality for optimal mri diagnostic device applied to parameter changes with 3D FRE at 1.5 T and 3.0 T,” International Journal of Multimedia and Ubiquitous Engineering, vol.14, no.2, pp.1-6, (2019) DOI:10.21742/IJMUE.2019.14.2.01(CrossRef)(Google Scholar)
[11] Cao Bu-Qing, Liu Jian-Xun, “Currency recognition modeling research based on BP neural network improved by Gene algorithm,” 2010 Second International Conference on Computer Modeling and Simulation, (2010) DOI: 10.1109/ICCMS.2010.270(CrossRef)(Google Scholar)