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Automatic writer identification framework for online handwritten documents using character prototypes
Authors:Guo Xian Tan  Christian Viard-Gaudin  Alex C Kot[Author vitae]
Affiliation:aNanyang Technological University Singapore;bIRCCyN/UMR CNRS 6597, Ecole Polytechnique de l’Université de Nantes, France
Abstract:This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported.
Keywords:Writer identification  Information retrieval  Online handwriting  Fuzzy c-means  Allographs
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