Citation
Shamsuddin, Siti Mariyam
(2000)
Higher Order Centralised Scale-Invariants for Unconstrained Isolated Handwritten Digits.
Doctoral thesis, Universiti Putra Malaysia.
Abstract
The works presented in this thesis are mainly involved in the study of global analysis of
feature extractions. These include invariant moments for unequal scaling in x and y
directions for handwritten digits, proposed method on scale-invariants and shearing
invariants for unconstrained isolated handwritten digits. Classifications using
Backpropagation model with its improved learning strategies are implemented in this
study. Clustering technique with Self Organising Map (SOM) and dimension reduction
with Principal Component Analysis (peA) on proposed invariant moments are also
highlighted in this thesis.
In feature extraction, a proposed improved formulation on scale-invariant moments is given
mainly for unconstrained handwritten digits based on regular moments technique. Several
types of features including algebraic and geometric invariants are also discussed. A computational comparison of these features found that the proposed method is superior
than the existing feature techniques for unconstrained isolated handwritten digits.
A proposed method on invariant moments with shearing parameters is also discussed. The
formulation of this invariant shearing moments have been tested on unconstrained isolated
handwritten digits. It is found that the proposed shearing moment invariants give good
results for images which involved shearing parameters.peA is used in this study to reduce the dimension complexity of the proposed moments
scale-invariants. The results show that the convergence rates of the proposed scaleinvariants
are better after reduction process using peA. This implies that the peA is an
alternative approach for dimension reduction of the moment invariants by using less
variables for classification purposes. The results show that the memory storage can be
saved by reducing the dimension of the moment invariants before sending them to the
classifier. In addition, classifications of unconstrained isolated handwritten digits are
extended using clustering technique with SOM methodology. The results of the study
show that the clustering of the proposed moments scale-invariants is better visualised with
SOM.
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