Learning Visual Shape Lexicon for Document Image Content Recognition
Identifieur interne : 000C18 ( Main/Exploration ); précédent : 000C17; suivant : 000C19Learning Visual Shape Lexicon for Document Image Content Recognition
Auteurs : Guangyu Zhu [États-Unis] ; Xiaodong Yu [États-Unis] ; Yi Li [États-Unis] ; David Doermann [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.
Url:
DOI: 10.1007/978-3-540-88688-4_55
Affiliations:
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We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li></region><settlement><li>College Park (Maryland)</li></settlement><orgName><li>Université du Maryland</li></orgName></list><tree><country name="États-Unis"><region name="Maryland"><name sortKey="Zhu, Guangyu" sort="Zhu, Guangyu" uniqKey="Zhu G" first="Guangyu" last="Zhu">Guangyu Zhu</name></region><name sortKey="Doermann, David" sort="Doermann, David" uniqKey="Doermann D" first="David" last="Doermann">David Doermann</name><name sortKey="Li, Yi" sort="Li, Yi" uniqKey="Li Y" first="Yi" last="Li">Yi Li</name><name sortKey="Yu, Xiaodong" sort="Yu, Xiaodong" uniqKey="Yu X" first="Xiaodong" last="Yu">Xiaodong Yu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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