Document zone content classification and its performance evaluation

Yalin Wang; Ihsin T. Phillips; Robert M. Haralick

doi:10.1016/j.patcog.2005.06.009

Document zone content classification and its performance evaluation

Yalin Wang, Ihsin T. Phillips, Robert M. Haralick

Research output: Contribution to journal › Article › peer-review

55 Scopus citations

Abstract

This paper describes an algorithm for the determination of zone content type of a given zone within a document image. We take a statistical based approach and represent each zone with 25 dimensional feature vectors. An optimized decision tree classifier is used to classify each zone into one of nine zone content classes. A performance evaluation protocol is proposed. The training and testing data sets include a total of 24,177 zones from the University of Washington English Document Image database III. The algorithm accuracy is 98.45% with a mean false alarm rate of 0.50%.

Original language	English (US)
Pages (from-to)	57-73
Number of pages	17
Journal	Pattern Recognition
Volume	39
Issue number	1
DOIs	https://doi.org/10.1016/j.patcog.2005.06.009
State	Published - Jan 2006
Externally published	Yes

Keywords

Background analysis
Decision tree classifier
Document image analysis
Document layout analysis
Pattern recognition
Viterbi algorithm
Zone content classification

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patcog.2005.06.009

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title = "Document zone content classification and its performance evaluation",

abstract = "This paper describes an algorithm for the determination of zone content type of a given zone within a document image. We take a statistical based approach and represent each zone with 25 dimensional feature vectors. An optimized decision tree classifier is used to classify each zone into one of nine zone content classes. A performance evaluation protocol is proposed. The training and testing data sets include a total of 24,177 zones from the University of Washington English Document Image database III. The algorithm accuracy is 98.45% with a mean false alarm rate of 0.50%.",

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AB - This paper describes an algorithm for the determination of zone content type of a given zone within a document image. We take a statistical based approach and represent each zone with 25 dimensional feature vectors. An optimized decision tree classifier is used to classify each zone into one of nine zone content classes. A performance evaluation protocol is proposed. The training and testing data sets include a total of 24,177 zones from the University of Washington English Document Image database III. The algorithm accuracy is 98.45% with a mean false alarm rate of 0.50%.

KW - Background analysis

KW - Decision tree classifier

KW - Document image analysis

KW - Document layout analysis

KW - Pattern recognition

KW - Viterbi algorithm

KW - Zone content classification

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JF - Pattern Recognition

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Document zone content classification and its performance evaluation

Abstract

Keywords

ASJC Scopus subject areas

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