Handwritten signatures recognizer by its envelope and strokes layout using HMM´s

Author

Sánchez, José A. ; Travieso, Carlos M. ; Alonso, Itizar G. ; Ferrer, Miguel A.

Author_Institution

Dept. de Senales y Comunicaciones, Univ. de Las Palmas, Spain

fYear

2001

fDate

37165

Firstpage

267

Lastpage

271

Abstract

A method for the automatic recognition of offline handwritten signatures using both global and local features is described. As global features, we use the envelope of the signature sequenced as polar coordinates; and as local features we use points located inside the envelope that describe the density or distribution of signature strokes. Each feature is processed as a sequence by a hidden Markov Model (HMM) classifier. The results of both classifiers are linearly combined, obtaining a recognition ratio of 95.15% with a database of 60 handwritten signatures

Keywords

handwriting recognition; handwritten character recognition; hidden Markov models; image recognition; HMM; automatic recognition; global features; handwritten signature database; handwritten signature recognizer; hidden Markov model classifier; local features; offline handwritten signature recognition; polar coordinates; recognition ratio; signature strokes; stroke layout; Acceleration; Automatic speech recognition; Fast Fourier transforms; Handwriting recognition; Hidden Markov models; Karhunen-Loeve transforms; Shape; Spatial databases; Testing; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Security Technology, 2001 IEEE 35th International Carnahan Conference on

Conference_Location

London

Print_ISBN

0-7803-6636-0

Type

conf

DOI

10.1109/.2001.962843

Filename

962843