Two Texture Segmentation of Document Image Using Wavelet Packet Analysis

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 featu...

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Bibliographic Details
Published inThe 9th International Conference on Advanced Communication Technology Vol. 1; pp. 395 - 398
Main Authors Geum-Boon Lee, Odoyo, W.O., Jae-Hoon Lee, Il-Yong Chung, Beom-Joon Cho
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2007
Subjects
Algorithm design and analysis
Clustering algorithms
document image segmentation
energy estimation
Image analysis
Image segmentation
Image texture analysis
k-means clustering algorithm
Signal analysis
Text analysis
Unsupervised learning
Wavelet analysis
wavelet packet analysis
Wavelet packets
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Summary: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.
ISBN:8955191316
9788955191318
ISSN:1738-9445
DOI:10.1109/ICACT.2007.358379