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.

In recent days, damages to information systems and network due to worm and virus using vulnerabilities of windows security have been rapidly increasing. How to deal with the attack using vulnerabilities of windows program is to install patch appropriately and rapidly. This study suggests security patch auto-management system which installs security patch file automatically to clients through automatic downloading of the patch from MS download center based on XML as existing patch management system needs intervention of managers.

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A Robust Digital Watermarking based on Virtual Optics Geum-boon Lee* • Beom-joon Cho** 요 약