The main objective of this project is to classify original or fake note, and if the note is original then need to find the denomination of the note.
Counterfeiting of paper currency is a major issue around the world. Almost every country has been severely impacted by this, which has escalated into a major issue. The major goal of this research is to identify Indian paper currency. We acquired a dataset of the currency notes for both original and fake notes of different denominations from internet.
By using feature extraction method of front side of the currency to classify whether the currency is original or fake. In this paper we used the Support Vector Machine (SVM) algorithm for the classification. Perceive whether something is Original or fake. We have used the MATLAB image processing toolbox.
Image processing is a method of enhancing an image's visual information for machine or hardware perception. And to classify the denomination of the note we used a CNN network and the average accuracy of the trained network is around 90% with minimal error.
Keywords: - Image Processing, Monetary Identification, Denomination, Currency Identification, Machine learning, SVM, Brisk Features, Convolutional Neural Network.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Software: Matlab 2020a or above
Hardware:
Operating Systems:
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB