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. In this paper, we first propose an enhanced background thinning based page segmentation approach. We 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 Komogrove complexity measure, and a neural network classijier 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.
Original language | English |
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Title of host publication | Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001 |
Pages | 279-282 |
Number of pages | 4 |
Publication status | Published - 1 Dec 2001 |
Event | 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001 - Hong Kong, Hong Kong Duration: 2 May 2001 → 4 May 2001 |
Conference
Conference | 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 2/05/01 → 4/05/01 |
ASJC Scopus subject areas
- General Computer Science