Font identification using visual global context

Siamak Khoubyari; Jonathan J. Hull

doi:10.1117/12.171099

23 March 1994 Font identification using visual global context

Siamak Khoubyari, Jonathan J. Hull

Proceedings Volume 2181, Document Recognition; (1994) https://doi.org/10.1117/12.171099
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

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.

Citation Download Citation

Siamak Khoubyari and Jonathan J. Hull "Font identification using visual global context", Proc. SPIE 2181, Document Recognition, (23 March 1994); https://doi.org/10.1117/12.171099

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