Discretization of integrated moment invariants for Writer Identification
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Embedded scale united moment invariant for identification of handwriting individuality
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Visual verification of historical chinese calligraphy works
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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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.