In this paper, we evaluate the performance of text- independent writer identification methods on a handwriting dataset containing medieval English documents. Applicable identification rates are achieved by combining textural features (joint directional probability distributions) with allographic features (grapheme-emission distributions). The aim is to develop an automatic handwriting identification tool that can assist the paleographer in the task of determining the authorship of historical manuscripts.
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Bulacu et al. (2007) studied this question.
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