CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1998 vol.20 Issue No.08 - August
Issue No.08 - August (1998 vol.20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.709616
<p><b>Abstract</b>—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.</p>
Optical font recognition, typographical features, font models, multivariate Bayesian classifier, document analysis, OCR.
Abdelwahab Zramdini, Rolf Ingold, "Optical Font Recognition Using Typographical Features", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.20, no. 8, pp. 877-882, August 1998, doi:10.1109/34.709616