Extracting individual features from moments for Chinese writer identification

Abstract

To solve the problem of writer identification (WI) with indeterminate classes (writers) and objects (characters), it is a good way to extract individual features with clear physical meanings and small dynamic ranges. In this paper, a new method named Moment-Based Feature Method to identify Chinese writers is presented in which normalized individual features are derived from geometric moments of character images. The extracted features are invariant under translation, scaling, and stroke-width. They are explicitly corresponding to human perception of shape and distribute their values in small dynamic ranges. Experiments of writer recognition and verification are implemented to demonstrate the efficiency of this method and promising results have been achieved.