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Two Geometric Algorithms for Layout Analysis

Thomas M. Breuel
- pp 188-199
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TLDR
Geometric algorithms for solving two key problems in layout analysis: finding a cover of the background whitespace of a document in terms of maximal empty rectangles and finding constrained maximum likelihood matches of geometric text line models in the presence of geometric obstacles are presented.
Abstract
This paper presents geometric algorithms for solving two key problems in layout analysis: finding a cover of the background whitespace of a document in terms of maximal empty rectangles, and finding constrained maximum likelihood matches of geometric text line models in the presence of geometric obstacles. The algorithms are considerably easier to implement than prior methods, they return globally optimal solutions, and they require no heuristics. The paper also introduces an evaluation function that reliably identifies maximal empty rectangles corresponding to column boundaries. Combining this evaluation function with the two geometric algorithms results in an easy-to-implement layout analysis system. Reliability of the system is demonstrated on documents from the UW3 database.

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References
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Journal ArticleDOI

Segmentation of Page Images Using the Area Voronoi Diagram

TL;DR: It is confirmed that the proposed method of page segmentation based on the approximated area Voronoi diagram is effective for extraction of body text regions, and it is as efficient as other methods based on connected component analysis.
Journal ArticleDOI

Statistical Approaches to Feature-Based Object Recognition

TL;DR: Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations, which motivates the development of new maximum-likelihood and MAP recognition formulations which are based on normal feature models.
Proceedings ArticleDOI

Image segmentation by shape-directed covers

TL;DR: A technique for image segmentation using shape-directed covers is described and applied to the fully automatic analysis of complex printed-page layouts, which for some tasks is superior to strategies currently emphasized in the literature, including bottom-up and top-down.
Proceedings ArticleDOI

Fast recognition using adaptive subdivisions of transformation space

TL;DR: Its performance is better than that of alignment and Hough transform methods, and, as opposed to these methods; RAST finds solutions satisfying simple, well-defined bounded error criteria.
Journal ArticleDOI

Background structure in document images

TL;DR: In this article, a method for analyzing the structure of the white background in document images is described, along with applications to the problem of isolating blocks of machine-printed text, based on computational-geometry algorithms for off-line enumeration of maximal white rectangles and on-line rectangle unification.
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