A Framework based on Semi-Supervised Clustering for Discovering Unique Writing Styles
A., Bharath; Madhvanath, Sriganesh
Keyword(s): writing style identification, semi-supervised stroke clustering, online hand writing recognition, online Devanagari character recognition
Abstract: 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 'semi-supervised clustering' 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.
Additional Publication Information: Published in the 10th International Conference on Document analysis and Recognition (ICDAR 2009), Barcelona, Spain, July 26-29, 2009.
External Posting Date: September 21, 2009 [Fulltext]. Approved for External Publication
Internal Posting Date: September 21, 2009 [Fulltext]