Title :
Optical font recognition using typographical features
Author :
Zramdini, A. ; Ingold, Rolf
Author_Institution :
A2i SA, Oron la Ville, Switzerland
fDate :
8/1/1998 12:00:00 AM
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
Keywords :
Bayes methods; optical character recognition; statistical analysis; OCR; global typographical features; multivariate Bayesian classifier; optical character recognition; optical font recognition; statistical approach; text length sensitivity; type size identification; type slope identification; type weight identification; typeface identification; typographical features; Bayesian methods; Character recognition; Feature extraction; Image recognition; Optical character recognition software; Optical design; Optical sensors; Robustness; Text analysis; Text recognition;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
