A Survey on Revolutionizing Document Security: A Comprehensive Deep Learning Approach For Signature Detection and Verification

Authors

  • Prof. V. S. Nalawade  Department of Computer Engineering, S. B. Patil College of Engineering, Indapur, Pune, Maharashtra, India
  • Yash P. Aoute  Department of Computer Engineering, S. B. Patil College of Engineering, Indapur, Pune, Maharashtra, India
  • Anuj S. Dharurkar  Department of Computer Engineering, S. B. Patil College of Engineering, Indapur, Pune, Maharashtra, India
  • Rushikesh D. Gunavare  Department of Computer Engineering, S. B. Patil College of Engineering, Indapur, Pune, Maharashtra, India

Keywords:

Document Security, Signature Detection, Signature Verification, Deep Learning, Convolutional Neural Networks (CNNs), Biometric Authentication, Forgery Detection

Abstract

In today's fast-paced business environment, automating signature verification is essential for efficiency. This project employs cutting-edge deep learning techniques: YOLOv5 for signature detection, CycleGAN for noise reduction, and VGG16-based feature extraction for verification. The workflow consists of three phases: signature detection, noise removal, and verification using cosine similarity with a threshold of 0.8. This interdisciplinary approach enhances operational efficiency and accuracy in document management and authentication, making it valuable for businesses.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-10-30

Issue

Volume 9, Issue 10, September-October-2023

Section

Research Articles

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How to Cite

[1]
Prof. V. S. Nalawade, Yash P. Aoute, Anuj S. Dharurkar, Rushikesh D. Gunavare, " A Survey on Revolutionizing Document Security: A Comprehensive Deep Learning Approach For Signature Detection and Verification" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 10, pp.18-24, September-October-2023.