Currency Detection using Similarity Indices Method using OpenCV

Description

This paper propose an image processing technique to extract paper currency denomination .Automatic detection and recognition of Indian currency note has gained a lot of research attention in recent years particularly due to its vast potential applications. It is shown that Indian currencies can be classified based on a set of unique non discriminating features. First we acquire the image by simple flat scanner on fix dpi with a particular size, the pixels level is set to obtain mage. The dominant colour and the aspect ratio of the note are extracted. After this extracted the portion of the note containing the unique shape, number, emblem, etc. This technique is used to match or find currency denomination of paper currency


Introduction

Technology is growing very fast these days. Consequently the banking sector is also getting modern day by day. This brings a deep need of automatic fake currency detection in automatic teller machine and automatic goods seller machine. Many researchers have been encouraged to develop robust and efficient automatic currency detection machine. Automatic machine which can detect banknotes are now widely used in dispensers of modern products like candies, soft drinks bottle to bus or railway tickets. The technology of currency recognition basically aims for identifying and extracting visible and invisible features of currency notes. Until now, many techniques have been proposed to identify the currency note. But the best way is to use the visible features of the note. For example, color and size. But this way is not helpful if the note is dirty or torn. If a note is dirty, its color characteristic is changed widely. So it is important that how we extract the features of the image of the currency note and apply proper algorithm to improve accuracy to recognize the note

System Analysis

Existing Systems

  • In the existing system, classification is done through simple image processing to classify based on color, shape and other parameters.

Disadvantages:

  • It has poor discriminatory power
  • It is poor to characterize the excellence data

Proposed system:

  • In this proposed system, Deep learning is used. First take the input of the given image and preprocessed the given image and convert the RGB image into the gray scale image. The extracted features can be used for recognition, classification and retrieval of currency notes.

Advantages:

  • Accuracy is more
  • Less distortion rate
  • Take very less time for execute

Block diagram:

Currency Detection using Machine Learning Opencv and Python


PREPROCESSING

Image Pre-processing?is a common name for operations with?images?at the lowest level of abstraction. Its input and output are intensity?images. The aim of?pre-processing?is an improvement of the?image?data that suppresses unwanted distortions or enhances some image?features important for further processing.

Image restoration is the operation of taking a corrupted/noisy image and estimating the clean original image. Corruption may come in many forms such as motion blur, noise, and camera misfocus.? Image restoration is different from image enhancement in that the latter is designed to emphasize features of the image that make the image more pleasing to the observer, but not necessarily to produce realistic data from a scientific point of view. Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbour procedure) provided by “Imaging packages” use no a priori model of the process that created the image.? With image enhancement noise can be effectively be removed by sacrificing some resolution, but this is not acceptable in many applications. In a Fluorescence Microscope resolution in the z-direction is bad as it is. More advanced image processing techniques must be applied to recover the object.? De-Convolution is an example of image restoration method. It is capable of: Increasing resolution, especially in the axial direction removing noise increasing contrast.

Median Filter Process:

The median of a distribution is the value for which larger and smaller values are equally probable. To calculate the median of a list of sample values, sort them in any order, and then peek the central value, or the mean between the two central values if the list is even-sized. If your list of values has a strong central tendency, which manifests itself as a single, well defined peak on the histogram, then the median is a good estimator of the peak position. If the distribution has no central peak, or if it is bimodal (two peaks), then the median is mostly meaningless.

Applied to images, median calculation leads to a useful morphological filter. First define a neighbourhood for each pixel in the image. This can be as simple as taking a square box cantered on each pixel, or can be somewhat more complex. Anyway, each pixel will have its own associated neighbourhood, which will consist on a given number of surrounding source pixels. Now calculate the median of each neighbourhood and store all of them in a safe place. Finally, replace each pixel with the median value of its associated neighbourhood.

Gray-Level Co-Occurrence Matrix:

To create a GLCM, use the?graycomatrix?function. The?graycomatrix?function creates a gray-level co-occurrence matrix (GLCM) by calculating how often a pixel with the intensity (gray-level) value?i?occurs in a specific spatial relationship to a pixel with the value?j. By default, the spatial relationship is defined as the pixel of interest and the pixel to its immediate right (horizontally adjacent), but you can specify other spatial relationships between the two pixels. Each element (i,j) in the resultant?GLCM is simply the sum of the number of times that the pixel with value?i?occurred in the specified spatial relationship to a pixel with value?j?in the input image. Because the processing required to calculate a GLCM for the full dynamic range of an image is prohibitive,?graycomatrix?scales the input image. By default,?graycomatrix?uses scaling to reduce the number of intensity values in gray scale image from 256 to eight. The number of gray levels determines the size of the GLCM. To control the number of gray levels in the GLCM and the scaling of intensity values, using the?Num Levels?and the?Gray Limits parameters of the?graycomatrix?function.


REQUIREMENTS ANAYLSIS

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

SOFTWARE REQUIREMENTS

  • Python
  • Anaconda Navigator
  • Python built-in modules

REFERENCE:

[1] Central Intelligence Agency. World Factbook Currency Exchange Rates. URL:https://www.cia.gov/library/publications/the-worldfactbook/fields/2076.html.

[2] Muhannad Alfarras. ?Bahraini paper currency recognition?. In: Journal of Advanced Computer Science and Technology Research 2.2 (2012), pp. 104?115.

[3] Hamid Hassanpour and Payam M. Farahabadi. ?Using Hidden Markov odels for paper currency recognition?. In: Expert Systems with Applications 36.6 (2009), pp. 10105?10111.

[4] Vipin Kumar Jain and Ritu Vijay. ?Indian currency denomination identification using image processing technique?. In: (2013).

[5] Smarti Kotwal. ?Image processing based heuristic analysis for enhanced currency recognition?. In: International Journal of Advancements in Technology 2.1 (2011), pp. 82?89.


 

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