Two Dimensional Auto Regressive Model for Hand Writing Recognition

M. Tarique; G. Malik

Abstract: The problem of writer identification in a multi-script environment is attempted using a two dimensional (2D) autoregressive (AR) modeling technique. Each writer is represented by a set of 2D AR model coefficients. A method to estimate AR model coefficients is proposed. This method is applied to an image of text written by a specific writer so that AR coefficients are obtained to characterize the writer. For a given sample, AR coefficients are computed and its L2 distance with each of the stored (writer) prototypes identifies the writer for the sample. The method has been tested on datasets of two different scripts, namely RIMES containing 382 French writers and ISI consisting of samples from 40 Bengali writers.

Keywords: AR Coefficient, Classification, Hand writing, Pattern Recognition