Forgery Numeral Handwriting Detection Based on Convolutional Neural Network

Project Code :TMMAAI50

Objective

Handwriting forgery detection is one of the hot spots in forensic science, and economic cases of handwritten forged numbers are increasing. At the same time, forgery identification of documents is an important piece of evidence in criminal proceedings. Hence, in this process forgery detection is done using deep learning algorithms.

Abstract

In this work, hand written forgery is detected/classified using Convolutional Neural Network (CNN). Handwriting forgery detection is one of the hotspots in forensic science, and economic cases of handwritten forged numbers are increasing. At the same time, forgery identification of documents is an important evidence in criminal proceedings. 

For the problem of tedious and low degree of automation of manual document inspection, put forward a method for handwritten forged numeral detection based on convolutional neural networks. Here in this project, we will classify six types of handwritten forged numbers using Convolutional Neural Networks (CNN). Experimental results show that this model is better than Support Vector Machine (SVM) feature classifier.

Keywords: Convolutional Neural Networks, Forensic Science, Forgery Detection, Support Vector Machine.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software & Hardware Requirements:

Software: Matlab 2018a or above

Hardware:

Operating Systems:

Windows 10

Windows 7 Service Pack 1

Windows Server 2019

Windows Server 2016

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

Learning Outcomes

  • Introduction to Matlab
  • What is EISPACK & LINPACK
  • 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 and classify the forgery 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

Demo Video

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