Online writer identification using alphabetic information clustering

Guo Xian Tan; Christian Viard-Gaudin; Alex C. Kot

doi:10.1117/12.805644

19 January 2009 Online writer identification using alphabetic information clustering

Guo Xian Tan, Christian Viard-Gaudin, Alex C. Kot

Proceedings Volume 7247, Document Recognition and Retrieval XVI; 72470F (2009) https://doi.org/10.1117/12.805644
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

Writer identification is a topic of much renewed interest today because of its importance in applications such as writer adaptation, routing of documents and forensic document analysis. Various algorithms have been proposed to handle such tasks. Of particular interests are the approaches that use allographic features [1-3] to perform a comparison of the documents in question. The allographic features are used to define prototypes that model the unique handwriting styles of the individual writers. This paper investigates a novel perspective that takes alphabetic information into consideration when the allographic features are clustered into prototypes at the character level. We hypothesize that alphabetic information provides additional clues which help in the clustering of allographic prototypes. An alphabet information coefficient (AIC) has been introduced in our study and the effect of this coefficient is presented. Our experiments showed an increase of writer identification accuracy from 66.0% to 87.0% when alphabetic information was used in conjunction with allographic features on a database of 200 reference writers.

Citation Download Citation

Guo Xian Tan, Christian Viard-Gaudin, and Alex C. Kot "Online writer identification using alphabetic information clustering", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470F (19 January 2009); https://doi.org/10.1117/12.805644

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