Machine Learning Techniques for Detecting False Signatures

  • Mihai Teletin Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania

Abstract

Deciding whether a handwritten signature is legit or it has been falsified is a very complex task. Several methods have been tried out by the graphology experts in order to detect such fraud. However, it is obvious that it is very hard to perform such a classification. In this paper we investigate the possibility to use some supervised learning techniques in order to build models capable to accurately perform such an analysis. The results reported during the testing phase of the obtained model are encouraging for further work.

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Published
2017-05-28
How to Cite
TELETIN, Mihai. Machine Learning Techniques for Detecting False Signatures. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 62, n. 1, p. 49-59, may 2017. ISSN 2065-9601. Available at: <http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/5>. Date accessed: 29 nov. 2020. doi: https://doi.org/10.24193/subbi.2017.1.04.
Section
Articles