Effects of Clustering Algorithms on Typographic Reconstruction

Type designers and historians studying the typefaces and fonts used in historical documents can usually only rely on available printed material. The initial wooden or metal cast fonts have mostly disappeared. In this paper we address the creation of character templates from printed documents. Images of characters scanned from Renaissance era documents are segmented, then clustered. A template is created from each obtained cluster of similar appearance characters. In order for subsequent typeface analysis tools to operate, the template should reduce the noise present in the individual instances by using information from the set of samples, but the samples must be homogeneous enough to not introduce further noise into the process. This paper evaluates the efficiency of several clustering algorithms and the associated parameters through cluster validity statistics and appearance of the resulting template image. Clustering algorithms that form tight clusters produce templates that highlight details, even though the number of available samples is smaller, while algorithms with larger clusters better capture the global shape of the characters.