Font retrieval on a large scale: An experimental study

This paper addresses the problem of font retrieval using a query-by-example paradigm: given a font, retrieve the the most visually similar fonts. We describe a font by (a) rendering a set of reference characters, (b) extracting a feature vector for each reference character and (c) concatenating the-level character descriptors. The similarity between two fonts is simply the similarity between the vectorial representations. Our contribution is an experimental comparison of character-level descriptors of step (b) on a large dataset of 9,000 fonts. The descriptors we chose to evaluate were drawn from the literature on typed and handwritten text analysis. An important conclusion is that the SIFT descriptor, which was shown to be state-of-the-art for object recognition in photographs and for handwriting recognition, yields the best results for font retrieval.