Core Points - Variable and Reduced Parameterization for Symbol Recognition
(2005)- Abstract
- Recent research in the field of on-line handwriting recognition has
been focused on statistical systems such as Hidden Markov Models,
Neural Networks or a combination of these. There are however merits
of employing an approach based on template matching. The first part
of this thesis presents a new strategy for parameterization of
on-line handwritten character samples. A novel efficient template
matching method enabled by this parameterization is also proposed.
In consecutive chapters of the thesis it is also shown that the
proposed structural parameterization enables an effective
application of template matching methods to the recognition of
cursive... (More) - Recent research in the field of on-line handwriting recognition has
been focused on statistical systems such as Hidden Markov Models,
Neural Networks or a combination of these. There are however merits
of employing an approach based on template matching. The first part
of this thesis presents a new strategy for parameterization of
on-line handwritten character samples. A novel efficient template
matching method enabled by this parameterization is also proposed.
In consecutive chapters of the thesis it is also shown that the
proposed structural parameterization enables an effective
application of template matching methods to the recognition of
cursive script. Ambiguity of the shapes of individual characters in
unconstrained cursive handwriting necessitates dictionary
interaction for real applications. A fast technique for applying
dictionary information to the language independent graph approach
has also been developed. A large data set of on-line cursive writing
has been collected and the developed system for mixed and cursive
on-line handwriting recognition has been shown to produce state of
the art results on this data set. One of the obvious potential
weaknesses of a structural parameterization technique such as the
one presented in this thesis is its sensitivity to digital noise in
the form of superfluous coordinates. Possible remedies to deal with
such effects have also been studied. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/635695
- author
- Sternby, Jakob LU
- supervisor
- organization
- publishing date
- 2005
- type
- Thesis
- publication status
- published
- subject
- pages
- 77 pages
- ISBN
- 91-628-6580-3
- language
- English
- LU publication?
- yes
- id
- 9e3b52c7-721b-4231-9fbf-e450881309d2 (old id 635695)
- date added to LUP
- 2016-04-04 09:29:13
- date last changed
- 2018-11-21 20:53:26
@misc{9e3b52c7-721b-4231-9fbf-e450881309d2,
abstract = {{Recent research in the field of on-line handwriting recognition has<br/><br>
been focused on statistical systems such as Hidden Markov Models,<br/><br>
Neural Networks or a combination of these. There are however merits<br/><br>
of employing an approach based on template matching. The first part<br/><br>
of this thesis presents a new strategy for parameterization of<br/><br>
on-line handwritten character samples. A novel efficient template<br/><br>
matching method enabled by this parameterization is also proposed.<br/><br>
In consecutive chapters of the thesis it is also shown that the<br/><br>
proposed structural parameterization enables an effective<br/><br>
application of template matching methods to the recognition of<br/><br>
cursive script. Ambiguity of the shapes of individual characters in<br/><br>
unconstrained cursive handwriting necessitates dictionary<br/><br>
interaction for real applications. A fast technique for applying<br/><br>
dictionary information to the language independent graph approach<br/><br>
has also been developed. A large data set of on-line cursive writing<br/><br>
has been collected and the developed system for mixed and cursive<br/><br>
on-line handwriting recognition has been shown to produce state of<br/><br>
the art results on this data set. One of the obvious potential<br/><br>
weaknesses of a structural parameterization technique such as the<br/><br>
one presented in this thesis is its sensitivity to digital noise in<br/><br>
the form of superfluous coordinates. Possible remedies to deal with<br/><br>
such effects have also been studied.}},
author = {{Sternby, Jakob}},
isbn = {{91-628-6580-3}},
language = {{eng}},
note = {{Licentiate Thesis}},
title = {{Core Points - Variable and Reduced Parameterization for Symbol Recognition}},
year = {{2005}},
}