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In this paper we present an image-based classification method for ancient Roman Republican coins that uses multiple sources of information. Exemplar-based classification, which estimates the coins' visual similarity by means of a dense correspondence field, and lexicon-based legend recognition are unified to a common classification approach. Classification scores from both coin sides are further integrated to an overall score determining the final classification decision. Experiments carried out on a dataset of 60 different classes comprising 464 coin images show that the combination of methods leads to higher classification rate than using them separately.

Improving Ancient Roman Coin Classification by Fusing Exemplar-Based Classification and Legend Recognition