Identification of currency with artificial intelligence techniques

Also Available Domains Deep Learning

Project Code :TMMAIP398


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.

Block Diagram


Software: Matlab 2020a or above


Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016


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


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


Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:

    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    •  Morphological processing
    • Segmentation etc.,

  • About Artificial Intelligence (AI)
  • About Machine Learning
  • About Deep Learning
  • About layers in AI (input, hidden and output layers)
  • Building AI (ANN/CNN) architecture using Matlab
  • We will able to know, what’s the term “Training” means in Artificial Intelligence
  • About requirements that can influence the AI training process:

    • Data
    • Training data
    • Validation data 
    • Testing data 
    • Hardware requirements to train network

  • How to detect an object using AI
  • How to extend our work to another real time applications
  • Project development Skills

    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

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