Unsupervised font clustering using stochastic versio of the em algorithm and global texture analysis

Abstract

An Unsupervised Font clustering technique is proposed in this work. The new approach is based on global texture analysis, using high order statistic features, Gaussian classifier and a stochastic version of the EM algorithm. The font recognition is performed by taking the document as a simple image, where one or several types of fonts are present. The identification is not performed letter by letter as with conventional approaches. In the proposed method a window analysis is employed to obtain the features of the document, using fourth and third order moments. The new technique does not involve a study of local typography; therefore, it is content independent. A detailed study was performed with 8 types of fonts commonly used in the Spanish language. Each type of font can have four styles that lead, to 32 font combinations. The font recognition with clean images is 100% accurate. © Springer-Verlag 2004.