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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2005.12
- 수록면
- 129 - 133 (5page)
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초록· 키워드
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In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, the local entropy is extracted and the document image is segmented to separate the background and the foreground using the Otsu’s method. Then we classify the segmented regions into each components using principle component analysis(PCA) algorithm base on the texture features that extracted with the co-occurrence matrix in the entropy image. The entropy based segmentation process robust to not only noise and change of light, but also skew and rotation. Contrary to the gray value, texture features don't restrict the form of the document and is superior to the discrimination of components. And contrary to the neural network, the PCA algorithm that use for the classifier can classify components robustly.
목차
Abstract
1. Introduction
2. Texture-based Segmentation and Classification
3. Experiments
4. Conclusion
REFERENCES
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