Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
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Article Number | 01074 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202339101074 | |
Published online | 05 June 2023 |
Offline Signature Verification Using Image Processing
1 Btech Student, Electronics and Communication Engineering, GRIET, Hyderabad, Telangana, India
2 Btech Student, Electronics and Communication Engineering, GRIET, Hyderabad, Telangana, India
3 Btech Student, Electronics and Communication Engineering, GRIET, Hyderabad, Telangana, India
4 Assistant Professor, Electronics and Communication Engineering, GRIET, Hyderabad, Telangana, India
* Corresponding Author: bushrajabeenshaik@gmail.com
A person’s signature is merely a handwritten sign that closely resembles his/her name, frequently stylized and distinctive, and that expresses the person’s identity, intent, and consent. Two types of verifications are present. They are online signature verification and offline signature verification. Generally, Offline Signature verification is less efficient and slower process compare to online verification when come to the situation having larger number of documents and files to verify with in less time. Over the years, many researchers have developed so many methods for signature verifications to help the people or organizations to find whether the signature of a particular person is forged or genuine. To overcome this problems; In this paper we introduced a simple method to improve the verification of the signature in Image Processing using Convolution Neural Networks(CNN).
Signature Verification it is used to authenticate various kinds of documents, including cheques, draughts, certificates, approvals, letters, and other legal ones, such verification is crucial for preventing document forgery and falsification. Previously, to verify a signature, it was manually checked against copies of real signatures. This straightforward approach might not be sufficient given that forgery and signature fraud techniques are becoming more sophisticated as a result of improving technology.
Key words: Offline Signature / Image Processing / Convolution Neural Networks / high accuracy / Speed and Robust
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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