Off-line signature verification using multiple neural network classification structures

Author

Papamarkos, Nikolaos ; Baltzakis, H.

Author_Institution

Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece

Volume

2

fYear

1997

fDate

2-4 Jul 1997

Firstpage

727

Abstract

This paper summarizes a research effort for an off-line signature recognition and verification system. The system uses three kinds of features extracted from the digital image of the signature: global features, grid information features and texture features. For each of them a special one-class-one-network classification structure has been implemented. In order for the system to come to a decision, it uses the results from all the three neural network structures, combined with a simple Euclidean norm

Keywords

feature extraction; handwriting recognition; image texture; learning (artificial intelligence); multilayer perceptrons; pattern classification; digital image; features extraction; global features; grid information features; multilayer perceptrons; neural network; pattern classification; signature recognition; signature verification; texture features; Circuit analysis computing; Data mining; Feature extraction; Handwriting recognition; Image segmentation; Laboratories; Neural networks; Noise reduction; Pattern recognition; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on

Conference_Location

Santorini

Print_ISBN

0-7803-4137-6

Type

conf

DOI

10.1109/ICDSP.1997.628455

Filename

628455