Image Processing CSE-4019 Distinction Between Fake Note and a Real Note

Image Processing CSE-4019 Distinction Between Fake Note and a Real Note

Shreya Tuli (VIT University, India), Gaurav Sharma (VIT University, India) and Nayan Mishr (VIT University, India)
DOI: 10.4018/978-1-5225-3643-7.ch010
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Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Its challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, and information privacy. Lately, the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. In this chapter, the authors distinguish between fake note and a real note and would like to take it to a level where it can be used everywhere. Its data after the detection of the fakeness and the real note can be stored in the database. The data to store will be huge. To overcome this problem, we can go for big data. It will help to store large amounts of data in no time. The difference between real note and fake note is that real note has its thin strip to be more or less continuous while the fake strip has fragmented thin lines in the strip. One could say that the fake note has more than one line in the thin strip while the real note only has one line. Therefore, if we see just one line, it is real, but if we see more than one line, it is fake. In this chapter, the authors use foreign currency.
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There are 6 different steps in order to distinguish between fake and original currency. In this project, we are using one of the famous segmentation techniques named thresholding. And also, our project involves various process like opening and closing etc…. In this we are using a real note and 2 fake notes and are differentiating between them based on the black strips it has.


It is the simplest method of image segmentation. From a grayscale image, thresholding can be used to create binary images. The simplest thresholding methods replace each pixel in an image with a black pixel if the image intensity is less than some fixed constant T, or a white pixel if the image intensity is greater than that constant. In the example image on the right, this results in the dark tree becoming completely black, and the white snow becoming completely white.


In mathematical morphology, opening is the dilation of the erosion of a set A by a structuring element B. Together with closing, this serves in computer vision and image processing as a basic workhorse of morphological noise removal. Opening removes small objects from the foreground (usually taken as the bright pixels) of an image, placing them in the background, while closing removes small holes in the foreground, changing small islands of background into foreground. These techniques can also be used to find specific shapes in an image. Opening can be used to find things into which a specific structuring element can fit (edges, corners, ...).

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