Kinetic Online Trajectory Recovery from Static Images of Handwriting
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2010-08-30
Language
en
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Abstract
In this Bachelor thesis I propose two new approaches
for extracting online handwriting data from scanned
images of handwriting (offline data) using the mechanical concept
of the momentum. The momentum describes the movement of
a body and can only be changed by exerting forces, as first
described by Sir I. Newton [1].
The first approach extracts pen strokes directly from an offline
handwriting by tracing lines with a tracing point that uses a
momentum. The second approach tries to sequence pen strokes
to whole pen trajectories by using a kinetic cost function that is
based on concepts derived from the definition of the momentum.
An exploration of the limits of the first approach shows that
it is not capable of dealing with noise that occurs in normal
handwriting. The kinetic cost function of the second approach is
compared to a traditional Euclidean distance based cost function
for stroke sequencing. Using the kinetic cost function for stroke
sequencing leads to significantly better pen trajectories than using
the Euclidean distance cost function. Using a momentum based
cost function for sequencing pen strokes can improve the quality
of extracted pen trajectories.
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Faculteit der Sociale Wetenschappen