Handwritten Signatures Recognizer by Its Envelope and Strokes Layout Using HMM's

JA Sanchez,CM Travieso, IG Alonso,MA Ferrer

Proceedings IEEE 35th Annual 2001 International Carnahan Conference on Security Technology (Cat No01CH37186)

Cited 17|Views0

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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.

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Key words

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

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