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Two Texture Segmentation of Document Image Using Wavelet Packet Analysis

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5 Author(s)
Geum-Boon Lee ; Department of Computer Engineering, University of Chosun Gwang-ju, Korea. E-mail: ; Wilfred O. Odoyo ; Jae-Hoon Lee ; Il-Yong Chung
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In this paper, we present a text segmentation method using wavelet packet analysis and k-means clustering algorithm. This approach assumes that the text and non-text regions are considered as two different texture regions. The text segmentation is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multiscale features, we compute the local energy and intensify the features before adapting the k-means clustering algorithm based on the unsupervised learning rule. The results show that our text segmentation method is effective for document images scanned from newspapers and journals.

Published in:

The 9th International Conference on Advanced Communication Technology  (Volume:1 )

Date of Conference:

12-14 Feb. 2007