Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37667
Title: Page segmentation and content classification for automatic document image processing
Authors: Yip, SK
Chi, Z 
Keywords: Computational complexity
Data compression
Document image processing
Image classification
Image coding
Image thinning
Neural nets
Issue Date: 2001
Source: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001, 02 May 2001-04 May 2001, Hong Kong, p. 279-282 How to cite?
Abstract: Page segmentation and image content classification is an important step for automatic document image processing including mixed-type document image compression, form and check reading, and mail sorting. The authors first propose an enhanced background thinning based page segmentation approach. They then present a hierarchical approach for the classification of the segmented sub-images into one of two categories: text and picture. The approach combines a cross-correlation method, the Kolmogorov complexity measure (A.N. Kolmogorov, 1965), and a neural network classifier in order to achieve both efficiency and high accuracy. Our approach has been tested on a number of mixed-type document images with good results
URI: http://hdl.handle.net/10397/37667
ISBN: 962-85766-2-3
DOI: 10.1109/ISIMP.2001.925388
Appears in Collections:Conference Paper

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