Writer identification using directional element features and linear transform

In this paper, we present an effective method to writer identification that is carried out using single Chinese character as script, which is very flexible and very easy to be used in practice. The directional element features are first extracted from the handwriting character scripts, then the dimensions of the features is reduced using PCA in order to cope with the small sample size problem. The most discriminative features are extracted from the reduced feature space using Fisher's Linear Discriminant Analysis. The Euclidian distance is proposed for classification. Experimental result verified the effectiveness of the proposed method.