Image Processing based Feature Extraction of Indian Currency Notes

Abstract

Counterfeit notes are a problem of almost every country but India has been hit really hard and has become a very acute problem. Fake Indian currency of 100, 500 and 1000 rupees seems to have flooded the whole system and there is no proper way to deal with them for a common person.There is a need to design a system that is helpful in recognition of paper currency notes with fast speed and in less time. The recognition system is composed of two parts. The first is preprocessing, including detecting edges, compressing data dimensionalities, and extracting features. The second one is recognition, in which the core is a neural network classifier. Number recognition is a challenging problem researchers had been research into this area for a long time. In our study there are many fields concern with numbers, for example, checks in banks or recognizing numbers in car plates, the subject of digit recognition appears. A system for recognizing isolated digits may be as an approach for dealing with such application. In other words, to let the computer understand the numbers and views them according to the computer process. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with handwriting number recognition problems such as, minimum distance, decision tree and statistics. In this thesis, I have proposed a new technique for extracting serial numbers of Indian paper currency which can be used for recognition or database purpose. I have used the MATLAB image processing toolbox to develop a software for this purpose. Image Processing involves changing the nature of an image in order to improve its pictorial information for human interpretation, for autonomous machine perception. 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. Previous related work in this field is also presented as a state of art literature survey. This technique can be used further in recognizing the currency notes with the help of neural network methods.