Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Verification of dynamic curves extracted from static handwritten scripts
Pattern Recognition
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HMM has been successfully used to model 1-D data, e.g. voice signals. Their use to model 2-D patterns was not as successful due to a major difficulty in describing the 2-D data using 1-D observation sequences. In this paper, we discuss the importance of this issue and present an improved method to extract 1-D observations from the dynamics of off-line handwritten words. The method is based on pen-trajectory estimation techniques. The paper also includes description of our HMM classifier which allows dynamic termination states to achieve enhanced discriminative power. Experimental results show the applicability and the usefulness of the proposed method. As a result of using the termination probability in HMM modeling, the top 1st recognition rate increased by 10%.