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

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.