Higher Order Centralised Scale-Invariants for Unconstrained Isolated Handwritten Digits

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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