A Framework Based on Semi-Supervised Clustering for Discovering Unique Writing Styles

An online multi-stroke character is often written in many ways. While some vary in the number of strokes they contain, others differ in the ordering of strokes. It is important for a writer-independent recognition system to learn these different styles of writing the character during the training phase in order to better model the training data. Typically, the samples of a character are clustered in an unsupervised manner and each cluster is modeled individually. In this paper, we describe an approach based on dasiasemi-supervised clusteringpsila where basic domain knowledge can be incorporated for better clustering of strokes present across all the characters.Experimental results show improved recognition accuracy when compared to the baseline system.