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Proceedings Paper

Textured reductions for document image analysis
Author(s): Dan S. Bloomberg
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Paper Abstract

A particularly effective method for analyzing document images, that consist of large numbers of binary pixels, is to generate reduced images whose pixels represent enhancements of textural densities in the full-resolution image. These reduced images are generated using an integrated combination of filtering and subsampling. Previously reported methods used thresholding over a square grid, and cascaded these threshold reduction operations. Here, the approach is generalized to a sequence of arbitrary filtering/subsample operations, with emphasis on several particular filtering operations that respond to salient textural qualities of document images, such as halftones, lines or blocks of text, and horizontal or vertical rules. As with threshold reductions, these generalized 'textured reductions' are performed with no regard for connected components. Consequently, the results are typically robust to noise processes that can vitiate analysis based on connected components. Examples of image analysis and segmentation operations using textured reductions are given. Some properties can be determined very quickly; for example, the existence or absence of halftone regions in a full page image can be established in about 10 milliseconds.

Paper Details

Date Published: 7 March 1996
PDF: 15 pages
Proc. SPIE 2660, Document Recognition III, (7 March 1996); doi: 10.1117/12.234697
Show Author Affiliations
Dan S. Bloomberg, Xerox Palo Alto Research Ctr. (United States)

Published in SPIE Proceedings Vol. 2660:
Document Recognition III
Luc M. Vincent; Jonathan J. Hull, Editor(s)

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