Estimating the Pen Trajectories of Static Handwritten Scripts using Hidden Markov Models
Date
2005-12
Authors
Nel, Emli-Mari
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : University of Stellenbosch
Abstract
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.
Description
Thesis (PhD (Electrical and Electronic engineering))--University of Stellenbosch, 2005.
Keywords
Theses -- Electronic engineering, Dissertations -- Electronic engineering