Subject:
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Cognitive artificial intelligence DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication |
Book title:
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BNAIC'07: Proceedings of the 19th Belgium-Netherlands Artificial Intelligence Conference 2007 |
Abstract:
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Writer identification is an important issue in forensic investigations of handwritten documents. A particularly
well-established method employed by forensic experts is to (visually) explore distinguishing features
of handwritten characters for comparing pieces of handwriting. Our research within the NWO Trigraph
project aims at automizing this laborious process. In this paper, we propose a novel method for identifying
a writer by means of features of loops and lead-in strokes of handwritten characters. Using a k-nearestneighbor
classifier, we were able to yield a correct identification performance of 98% on a database of 41
writers. These results are promising and have great potential for use in the forensic practice, where the
outcomes of handwritten document comparisons have to be justified via explainable features like the ones
explored in this paper.
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