Estimating the Pen Trajectories of Static Handwritten Scripts using Hidden Markov Models
Emli-Mari Nel · SUNScholar (Stellenbosch University) · 2005
I, the undersigned, hereby declare that the work contained in this dissertation is my own original work and that I have not previously in its entirety or in part submitted it at any university for a degree. Signature Date 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 ap-plied 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 informa-tion 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.