Text Localization and Recognition in Complex Scenes Using Local Features
Identifieur interne : 000569 ( Istex/Curation ); précédent : 000568; suivant : 000570Text Localization and Recognition in Complex Scenes Using Local Features
Auteurs : Qi Zheng [République populaire de Chine] ; Kai Chen [République populaire de Chine] ; Yi Zhou [République populaire de Chine] ; Congcong Gu [République populaire de Chine] ; Haibing Guan [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: We describe an approach using local features to resolve problems in text localization and recognition in complex scenes. Low image quality, complex background and variations of text make these problems challenging. Our approach includes the following stages: (1) Template images are generated automatically; (2) SIFT features are extracted and matched to template images; (3) Multiple single-character-areas are located using segmentation algorithm based upon multiple-size sliding sub-windows; (4) An voting and geometric verification algorithm is used to identify final results. This framework thus is essentially simple by skipping many steps, such as normalization, binarization and OCR, which are required in previous methods. Moreover, this framework is robust as only SIFT feature is used. We evaluated our method using 200,000+ images in 3 scripts (Chinese, Japanese and Korean). We obtained average single-character success rate of 77.3% (highest 94.1%), average multiple-character success rate of 63.9% (highest 89.6%).
Url:
DOI: 10.1007/978-3-642-19318-7_10
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Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name sortKey="Zhou, Yi" sort="Zhou, Yi" uniqKey="Zhou Y" first="Yi" last="Zhou">Yi Zhou</name><affiliation wicri:level="1"><mods:affiliation>School of Information Security Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Security Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name sortKey="Gu, Congcong" sort="Gu, Congcong" uniqKey="Gu C" first="Congcong" last="Gu">Congcong Gu</name><affiliation wicri:level="1"><mods:affiliation>School of Information Security Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Security Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name 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Localization and Recognition in Complex Scenes Using Local Features</title><author><name sortKey="Zheng, Qi" sort="Zheng, Qi" uniqKey="Zheng Q" first="Qi" last="Zheng">Qi Zheng</name><affiliation wicri:level="1"><mods:affiliation>School of Information Security Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Security Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name sortKey="Chen, Kai" sort="Chen, Kai" uniqKey="Chen K" first="Kai" last="Chen">Kai Chen</name><affiliation wicri:level="1"><mods:affiliation>School of Information Security Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Security Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name sortKey="Zhou, Yi" sort="Zhou, Yi" uniqKey="Zhou Y" first="Yi" last="Zhou">Yi Zhou</name><affiliation wicri:level="1"><mods:affiliation>School of Information Security Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Security Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name sortKey="Gu, Congcong" sort="Gu, Congcong" uniqKey="Gu C" first="Congcong" last="Gu">Congcong Gu</name><affiliation wicri:level="1"><mods:affiliation>School of Information Security Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Information Security Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author><author><name sortKey="Guan, Haibing" sort="Guan, Haibing" uniqKey="Guan H" first="Haibing" last="Guan">Haibing Guan</name><affiliation wicri:level="1"><mods:affiliation>Department of Computer Science and Engineering, Shanghai Jiao Tong University, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Computer Science and Engineering, Shanghai Jiao Tong University</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2011</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">9D128F2D3EA38B3DD86196B34990E95F06F2D27B</idno><idno type="DOI">10.1007/978-3-642-19318-7_10</idno><idno type="ChapterID">10</idno><idno type="ChapterID">Chap10</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: We describe an approach using local features to resolve problems in text localization and recognition in complex scenes. Low image quality, complex background and variations of text make these problems challenging. Our approach includes the following stages: (1) Template images are generated automatically; (2) SIFT features are extracted and matched to template images; (3) Multiple single-character-areas are located using segmentation algorithm based upon multiple-size sliding sub-windows; (4) An voting and geometric verification algorithm is used to identify final results. This framework thus is essentially simple by skipping many steps, such as normalization, binarization and OCR, which are required in previous methods. Moreover, this framework is robust as only SIFT feature is used. We evaluated our method using 200,000+ images in 3 scripts (Chinese, Japanese and Korean). We obtained average single-character success rate of 77.3% (highest 94.1%), average multiple-character success rate of 63.9% (highest 89.6%).</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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