Improve Efficiency of On-Line Handwriting Recognition Using Hidden Markov Models

Abstract

In this paper, as signatures continue to play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. to perform verification or identification of a signature, several steps must be performed. Online signature verification has been shown to achieve much higher verification rate than offline verification this paper proposes a novel framework for online signature verification. Different from previous methods, our approach makes use of online handwriting instead of handwritten images for registration. The online registrations enable robust recovery of the writing trajectory from an input online signature and thus allow effective shape matching between registration and verification signatures. in addition, the online registrations enable robust recovery of the writing trajectory from an input online signature and thus allow effective shape matching between registration and verification signatures. in addition, the features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden markov models, support vector machines, and euclidean distance classifier. We propose several new techniques to improve the performance of the new signature verification rate system.