A method for document zone content classification

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

A method for document zone content classification

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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%.

Original language	English (US)
Title of host publication	Proceedings - International Conference on Pattern Recognition
Pages	196-199
Number of pages	4
Volume	16
Edition	3
State	Published - 2002
Externally published	Yes

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Vision and Pattern Recognition
Hardware and Architecture

Cite this

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title = "A method for document zone content classification",

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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AB - 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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BT - Proceedings - International Conference on Pattern Recognition

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A method for document zone content classification

Abstract

ASJC Scopus subject areas

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