A method for document zone content classification
Yalin Wang,I.T. Phillips,Robert M. Haralick +2 more
- 11 Aug 2002
- Vol. 3, pp 30196-30196
TL;DR: The classification scheme uses an optimized binary decision tree and Viterbi algorithm for HMM to find the optimal solution and provides a protocol for its performance evaluation.
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Abstract: This paper describes an algorithm to classify each given document zone into one of nine classes and provides a protocol for its performance evaluation. The classification scheme uses an optimized binary decision tree and Viterbi algorithm for HMM to find the optimal solution. Our algorithm was trained and tested on a total of 24,177 zones within the 1600 images from UWCDROM III database. Its accuracy rate is 98.45% with a mean false alarm rate of 0.50%.
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Citations
Analysis and interpretation of visual saliency for document functional labeling
Véronique Eglin,Stéphane Bres +1 more
TL;DR: This approach considers visual human-based predicates to describe and identify text units according to their visual saliency and their perceptual attraction power on the reader’s eye to support a quick and robust process of functional labeling used to characterize text regions of document pages.
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Computer and Robot Vision
Robert M. Haralock,Linda G. Shapiro +1 more
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TL;DR: This two-volume set is an authoritative, comprehensive, modern work on computer vision that covers all of the different areas of vision with a balanced and unified approach.
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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