Detection of fake currency

1. Detection of Fake currency note using Machine Learning
Using Convolutional Neural Network Model is trained using different images of different currency
notes such as 2000rs note, 500rs note. First of select a target size like how much the image should be
like so that can scale it down to require more and fix number of bag sizes and define the classes but the
classes is like which notes we have like 100rs, 500rs. After running this it trains the module then it can
identify which is ever note we input like and which note it is can identified clearly.
Block Diagram:
False
True
Input
Preprocessing
True currency
is detected
Get features of
currency from
both the side
Compare it
using
Machine
learning
algorithm
Fake
currency is
detected
Does
features
matches

2. Steps Implementation:
Image Acquisition
output
comparison
Pre- Processing
Gray scale conversion
Feature contraction

3. The steps used for image preprocessing are :
Read image
Resize image
Remove noise(Denoise)
Segmentation
Morphology(smoothing edges)
Step 1: Read image
In this step, we save the path to our image dataset into a variable then created a function to
load the folders containing images into arrays.
Step 2: Resize image
In this step in order to visualize the change, we are going to create two functions to display the
images the first being a one to display one image and the second for two images. After that, we
then create a function called processing that just receives the images as a parameter. Then
some images may vary in size so we should give a base size for all images.
Step 3: Remove noise
To remove unwanted noise we use gaussian blur in this code. Gaussian function is used to
blurring an image to reduce the image noise.
Step 4: Segmentation
In this step we are going to separate the background from foreground objects and further
improve the segmenation with more noise removal.
Step 6: Morphology
In this step, we separate different objects in the image with markers.