Multiscale segmentation of unstructured document pages using soft decision integration

被引：54

作者：

Etemad, K ^{[1
]}

Doermann, D ^{[1
]}

Chellappa, R ^{[1
]}

机构：

[1] UNIV MARYLAND, INST ADV COMP STUDIES, COLLEGE PK, MD 20742 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1997年 / 19卷 / 01期

关键词：

document processing; multiscale analysis; context dependent classification; soft decision integration; wavelet packets; neural networks; CLASSIFICATION;

D O I：

10.1109/34.566817

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present an algorithm for layout-independent document page segmentation based on document texture using multiscale feature vectors and fuzzy local decision information. Multiscale feature vectors are classified locally using a neural network to allow soft/fuzzy multi-class membership assignments. Segmentation is performed by integrating soft local decision vectors to reduce their ''ambiguities.''

引用

页码：92 / 96

页数：5

共 16 条

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