Online text-independent writer identification based on Stroke's probability distribution function

Abstract

This paper introduces a novel method for online writer identification. Traditional methods make use of the distribution of directions in handwritten traces. The novelty of this paper comes from 1)We propose a text-independent writer identification that uses handwriting stroke's probability distribution function (SPDF) as writer features; 2)We extract four dynamic features to characterize writer individuality; 3)We develop new distance measurement and combine dynamic features in reducing the number of characters required for online text-independent writer identification. In particular, we performed comparative studies of different similarity measures in our experiments. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the new method can improve the identification accuracy and reduce the number of characters required. © Springer-Verlag Berlin Heidelberg 2007.