Writer identification of chinese handwriting using grid microstructure feature

Abstract

This paper proposes a histogram-based feature to Chinese writer identification. It is called grid microstructure feature. The feature is extracted from the edge image of the real handwriting image. The positions of edge pixel pairs are used to describe the characteristics in a local grid around every edge pixel. After global statistic, the probability density distribution of different pixel pairs is regarded as the feature representing the writing style of the handwriting. Then the similarity of two handwritings is measured with the improved weighted visions of some original metric. On the HIT-MW Chinese handwriting database involving 240 writers, the best Top-1 identification accuracy is 95.0% and the Top-20 accuracy reaches 99.6%. © Springer-Verlag Berlin Heidelberg 2009.