A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints
Identifieur interne : 000C59 ( Istex/Checkpoint ); précédent : 000C58; suivant : 000C60A Robust Text Segmentation Approach in Complex Background Based on Multiple Constraints
Auteurs : Libo Fu [République populaire de Chine] ; Weiqiang Wang [République populaire de Chine] ; Yaowen Zhan [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: In this paper we propose a robust text segmentation method in complex background. The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.
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DOI: 10.1007/11581772_52
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The proposed method first utilizes the K-means algorithm to decompose a detected text block into different binary image layers. Then an effective post-processing is followed to eliminate background residues in each layer. In this step we develop a group of robust constraints to characterize general text regions based on color, edge and stroke thickness. We also propose the components relation constraint (CRC) designed specifically for Chinese characters. Finally the text image layer is identified based on the periodical and symmetrical layout of text lines. The experimental results show that our method can effectively eliminate a wide range of background residues, and has a better performance than the K-means method, as well as a high speed.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Fu, Libo" sort="Fu, Libo" uniqKey="Fu L" first="Libo" last="Fu">Libo Fu</name></noRegion><name sortKey="Fu, Libo" sort="Fu, Libo" uniqKey="Fu L" first="Libo" last="Fu">Libo Fu</name><name sortKey="Fu, Libo" sort="Fu, Libo" uniqKey="Fu L" first="Libo" last="Fu">Libo Fu</name><name sortKey="Wang, Weiqiang" sort="Wang, Weiqiang" uniqKey="Wang W" first="Weiqiang" last="Wang">Weiqiang Wang</name><name sortKey="Wang, Weiqiang" sort="Wang, Weiqiang" uniqKey="Wang W" first="Weiqiang" last="Wang">Weiqiang Wang</name><name sortKey="Wang, Weiqiang" sort="Wang, Weiqiang" uniqKey="Wang W" first="Weiqiang" last="Wang">Weiqiang Wang</name><name sortKey="Zhan, Yaowen" sort="Zhan, Yaowen" uniqKey="Zhan Y" first="Yaowen" last="Zhan">Yaowen Zhan</name><name sortKey="Zhan, Yaowen" sort="Zhan, Yaowen" uniqKey="Zhan Y" first="Yaowen" last="Zhan">Yaowen Zhan</name><name sortKey="Zhan, Yaowen" sort="Zhan, Yaowen" uniqKey="Zhan Y" first="Yaowen" last="Zhan">Yaowen Zhan</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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