INCV: An Inception Network Based Deep Learning Framework for Signature Recognition

Authors

  • Hasibur Rahman Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3804, Bangladesh
  • K. M. Aslam Uddin Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3804, Bangladesh
  • Nusrat Jahan Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3804, Bangladesh
  • Apurba Adhikary Department of Information and Communication Engineering, Noakhali Science and Technology University, Noakhali, 3804, Bangladesh
  • Samrat Kumar Dey School of Science and Technology, Bangladesh Open University, Gazipur, 1705, Bangladesh
  • Monishanker Halder Department of Computer Science and Engineering, Jashore University of Science and Technology
  • Avi Deb Raha Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, 7408, Korea
  • Mrityunjoy Gain Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, 7408, Korea
  • Sumit Kumar Dam Department of Computer Science and Engineering, Khulna University, Khulna, 9208, Bangladesh
  • Yu Qiao Department of Artificial Intelligence, Kyung Hee University, Yongin-si, 7408, Korea
  • Anupam Kumar Bairagi Department of Computer Science and Engineering, Khulna University, Khulna, 9208, Bangladesh

DOI:

https://doi.org/10.53808/KUS.2024.21.02.1180-se

Keywords:

Signature Recognition, Convolutional Neural Networks, Inception Network V3, Inception-ResNet V2

Abstract

The signature of an individual is a handwritten sign or mark that resembles an individual’s name, is often stylized and unique, and indicates the person’s identity, intent, and consent. There are many cases where the signature may be forged by an anonymous person, which is one of the most complicated real-world problems and has significant social and commercial impacts. Given the widespread use of handwritten signatures in legal and financial transactions, it is imperative for researchers to carefully choose an effective method to verify these signatures and prevent forgeries, which can result in significant financial losses for customers. Although there has been a lot of study done on forgery detection and signature verification, the difficulty of detecting competent forgeries remains a major problem for both scholars and practitioners. This paper developed a strategy called Inception Network Customized Version (INCV) for signature recognition based on the two latest inception network architectures: Inception Network v3 and Inception ResNet v2. We have collected the signature images of individuals and worked with these pre-trained models to apply transfer learning to create our customized models. We have employed our customized versions to recognize the images of the individual’s signature. Comparative analysis between the two customized versions of the inception network gives a better approach for recognizing individuals’ signatures than the traditional approaches for recognizing signatures, where INCV I (based on Inception Network V3) gives 97% accuracy on the train set and 92% accuracy on the test set, however, INCV II (based on Inception ResNet V2) produced 98% accuracy on the train set and 96% accuracy on the test set.

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Published

18-12-2024

How to Cite

[1]
H. Rahman, “INCV: An Inception Network Based Deep Learning Framework for Signature Recognition”, Khulna Univ. Stud., pp. 95–102, Dec. 2024.

Issue

Section

Science and Engineering

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