Discretization of integrated moment invariants for writer identification

Abstract

Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of unconstrained words. Subsequently, the discrete proposed features undertake discretization procedure prior to classification for better feature representation and splendid classification accuracy. Collectively, discrete values are finite intervals in a continuous spectrum of values and well known to play important roles in data mining and knowledge discovery. Many induction algorithms found in the literature requires that training data contains only discrete features and some works better on discretized data; in particular rule based approaches like rough sets. Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. Successive exploration is executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer simulations are embraced to attest the feasibility of the proposed technique in Writer Identification. The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data