Optical font recognition using typographical features
Zramdini, A.
Ingold, R.
A2i SA, Oron la Ville;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 1998
Volume: 20,
Issue: 8
On page(s): 877-882
ISSN: 0162-8828
References Cited: 15
CODEN: ITPIDJ
INSPEC Accession Number: 6018671
Digital Object Identifier: 10.1109/34.709616
Current Version Published: 2002-08-06
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
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