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Proceedings Paper

Font identification using visual global context
Author(s): Siamak Khoubyari; Jonathan J. Hull
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Paper Abstract

An important part of many algorithms that convert digital images of machine-printed text into their ASCII equivalent is information about fonts. This paper presents an algorithm for identifying the font in which a document is printed. The algorithm matches word-level information gathered from the document image to fonts in an image database. This method is more robust in the presence of noise than font recognition algorithms that use character-level information. Clusters of frequent function words (such as the, of, and, a, and to) are constructed from an input document image. The clusters are then matched to a database of function words derived from document images, and the document that matches best provides the identification of the input font. This technique utilizes the context from many words in an input document to overcome noise. Experimental results are presented that show near-perfect recognition of fonts, even in noisy documents.

Paper Details

Date Published: 23 March 1994
PDF: 9 pages
Proc. SPIE 2181, Document Recognition, (23 March 1994); doi: 10.1117/12.171099
Show Author Affiliations
Siamak Khoubyari, SUNY/Buffalo (United States)
Jonathan J. Hull, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 2181:
Document Recognition
Luc M. Vincent; Theo Pavlidis, Editor(s)

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