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.
Conference Committee Involvement (7)
Media Watermarking, Security, and Forensics 2015
9 February 2015 | San Francisco, California, United States
Media Watermarking, Security, and Forensics 2014
3 February 2014 | San Francisco, California, United States
Media Watermarking, Security, and Forensics 2013
5 February 2013 | Burlingame, California, United States
Media Watermarking, Security, and Forensics 2012
23 January 2012 | Burlingame, California, United States
Media Watermarking, Security, and Forensics XIII
24 January 2011 | San Francisco Airport, California, United States
Visual Communications and Image Processing 2007
30 January 2007 | San Jose, CA, United States
Visual Communications and Image Processing 2006
17 January 2006 | San Jose, California, United States
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