Estimating the Pen Trajectories of Static Handwritten Scripts using Hidden Markov Models
Please cite this item using this persistent URLhttp://hdl.handle.net/10019.1/1159
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Individuals can be identified by their handwriting. Signatures are, for example, currently used as a biometric identifier on documents such as cheques. Handwriting recognition is also applied to the recognition of characters and words on documents—it is, for example, useful to read words on envelopes automatically, in order to improve the efficiency of postal services. Handwriting is a dynamic process: the pen position, pressure and velocity (amongst others) are functions of time. However, when handwritten documents are scanned, no dynamic information is retained. Thus, there is more information inherent in systems that are based on dynamic handwriting, making them, in general, more accurate than their static counterparts. Due to the shortcomings of static handwriting systems, static signature verification systems, for example, are not completely automated yet. During this research, a technique was developed to extract dynamic information from static images. Experimental results were specifically generated with signatures. A few dynamic representatives of each individual’s signature were recorded using a single digitising tablet at the time of registration. A document containing a different signature of the same individual was then scanned and unravelled by the developed system. Thus, in order to estimate the pen trajectory of a static signature, the static signature must be compared to pre-recorded dynamic signatures of the same individual. Hidden Markov models enable the comparison of static and dynamic signatures so that the underlying dynamic information hidden in the static signatures can be revealed. Since the hidden Markov models are able to model pen pressure, a wide scope of signatures can be handled. This research fully exploits the modelling capabilities of hidden Markovmodels. The result is a robustness to typical variations inherent in a specific individual’s handwriting. Hence, despite these variations, our system performs well. Various characteristics of our developed system were investigated during this research. An evaluation protocol was also developed to determine the efficacy of our system. Results are promising, especially if our system is considered for static signature verification.