Online Signature Verification Using Neural Network

Rohan Dattatraya Gaidhani , Rushikesh Aaba Thombare , Aarti Navnath Ghorpade , Unnati Kantilal Jadhav , Ms.Sneha A. Khaire

Offline handwritten signature, classification, algorithms, artificial intelligence, CNN

Biometrics is now widely used all over the world for the identification and verification of people and their signatures. A person’s handwritten signature is a unique identifying work of human that is primarily used and recognized in banking and other financial and legal operations. Handwritten signatures, on the other hand, are becoming increasingly valuable due to their historical significance as a target of deception. The Sign Verification System (SVS) tries to determine whether a sign is genuine (created by the specified individual) or forged (produced by an impostor).Using images of scanned signatures and other documents without dynamic information about the signing process has proven difficult, especially in offline (static) situations. The use of Deep Learning algorithms to learn feature signature picture representations has been well-documented in the literature over the last five to ten years. Here, we examine how the subject has been studied throughout the last few decades, as well as the most recent developments and future study plans

Published Paper   E-Certificate

"Online Signature Verification Using Neural Network", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.82 - 85, March-2023, Available :https://ijsdr.org/papers/IJSDR2303017.pdf

Volume 8 Issue 3, March-2023

Pages : 82 - 85

Paper Reg. ID: IJSDR_204365

Published Paper Id: IJSDR2303017

Downloads: 000347444

Research Area: Engineering

Country: -, -, -

Published Paper PDF: https://ijsdr.org/papers/IJSDR2303017

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2303017

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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