AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Authors: MIHAI TELETIN.
DOI: 10.24193/subbi.2017.1.04
Published Online: 2017-06-01
Published Print: 2017-06-01
pp. 49-59
VIEW PDF: MACHINE LEARNING TECHNIQUES FOR DETECTING FALSE SIGNATURES
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.
2010 Mathematics Subject Classification. 68T05, 62M45.1998 CR Categories and Descriptors. I.2.6 [Computing Methodologies]: Artificial Intelligence { Learning; I.2.8 [Computing Methodologies]: Artificial Intelligence { Problem solving.
Key words and phrases. Machine learning, Convolutional neural networks, Support vector machines, Classification, Signature verification.
