Abstract-A new statistical approach based on global typographical features is proposed to the widely neglected problem of font recognition. It aims at the identification of the typeface, weight, slope and size of the text from an image block without any knowledge of the content of that text. The recognition is based on a multivariate Bayesian classifier and operates on a given set of known fonts. The effectiveness of the adopted approach has been experimented on a set of 280 fonts. Font recognition accuracies of about 97 percent were reached on high-quality images. In addition, rates higher than 99.9 percent were obtained for weight and slope detection. Experiments have also shown the system robustness to document language and text content and its sensitivity to text length.
Zramdini et al. REF identify the typeface, weight, slope, and size of a font image and use a multivariate Bayesian classifier to classify the font.
Optical font recognition using typographical features
IEEE Trans. Pattern Anal. Mach. Intell.
IEEE Trans. Pattern Anal. Mach. Intell.
Computer Science