Currency Recognition System using Image Processing

Published on 28 May 2021. Written by Shirin Tikoo

It is puzzling for individuals to perceive each currency from various nations. Furthermore, past perceiving the currency note, we additionally need to perceive the genuine and fake currency notes that are available in the market. To determine this sort of issue we have a Currency Recognition System. It distinguishes the currency notes and the variety among genuine and counterfeit currency notes. Our present framework is taking a shot at image processing, methods that incorporate image filtering, edge identification, segmentation and a database for putting away the qualities of the currency note.

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Project Description

In this venture, we are going to utilize image processing strategies and a database.

Image Scanning and Reading: Image of the currency note is scanned, filtered and kept as JPEG (Joint photographic expert group) during examining, scanning and storing procedures of the image may lose some data and that can't be recouped. What's more, after that image is read using the MATLAB Code.

Image Smoothing: During the picture filtering, we can get some commotion (noise) over the image. So, to expel the commotion, image smoothing is being performed.

Grayscale and Binary Scale conversion: Grayscale expresses the hues i.e. colors going from dark to white though Binary scale expresses commonly two value i.e. high contrast and for appropriate division, we utilize the binary scale.

Image Segmentation: The outcome of the process image segmentation is that it makes little arrangement of fragments of the whole image. Every pixel here is comparable w.r.t a few traits or qualities, for example, shading, color, intensity, or texture. An adjacent area in the images is naturally different w.r.t the same properties.

Feature Extraction: The feature extraction process incorporates the extraction of the currency notes highlights. Feature extraction was an essential step impressively for currency recognition, which consequences for the structure and execution of the classifier successfully.

Fundamentally, at first occurrence, individuals may not focus on the exact details and indistinguishable attributes of the banknotes for their acknowledgment, rather they consider the basic qualities of banknotes, for example, the foundation shading, thickness of banknote, texture and feel present on the banknotes, size, and other minor details.

Pattern Matching: It is a process after segmentation where we can discover the contrast between the genuine and the fake notes.

MATLAB as a Programming language: MATLAB tool is used for image processing and for analyzing the final results.

Database: We have to make a little database for putting away the qualities like color, the intensity, serial number, etc. of the currency.

Project Implementation

There are a couple of steps for accomplishing the conclusive outcome and the means are:

1. The initial step is scanning and reading the image in the given format of the image i.e. JPEG format.
2. Secondly, we need to pre-process the image and remove noise
3. After noise removal, our next step is smoothening the image.
4. After smoothening, we do the image processing, edge detection, segmentation and pattern matching to get the correct result.
5. The final step is to print the results.

Software requirement

Programming language: MATLAB and any database.

Operating System - Windows, Linux

Kit required to develop Currency Recognition System using Image Processing:

• No Kit

Technologies you will learn by working on Currency Recognition System using Image Processing:

• Image Processing
• Computer vision

Any Questions?