PCA and LDA as Dimension Reduction for Individuality of Handwriting in Writer Verification

Principal Component Analysis and Linear Discriminant Analysis are the most popular approach used in statistical data analysis. Both of these approaches are usually implemented as traditional linear technique for Dimension reduction approach. Dimension reduction is useful approach in data analysis application. The concept of dimension reduction will help the process of identifying the most important features in handwritten data which also called as individuality of the handwriting. Where, this individuality will help the verification process in order to verify the handwritten document. The purposed of this paper is to perform both techniques above in writer verification process in order to acquire the individuality of the handwriting. Classification process will be use to evaluate the effectiveness of both approach performance in form of classification accuracy.