Complementary combination of holistic and component analysis for recognition of low-resolution video character images
Identifieur interne : 000D26 ( Main/Exploration ); précédent : 000D25; suivant : 000D27Complementary combination of holistic and component analysis for recognition of low-resolution video character images
Auteurs : Seonghun Lee [Corée du Sud] ; Jinhyung Kim [Corée du Sud]Source :
- Pattern recognition letters [ 0167-8655 ] ; 2008.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Video OCR aims at extracting text from video images in order to understand the context of the video. Video character images are usually given in low resolution with unique characteristics such as large stroke distortion, font variation, and variable size. Therefore, recognizing such characters in video images is very challenging. This is particularly true in the case of Chinese and Korean languages, where characters have complicated shapes and the number of classes (characters) is very large. In this paper, we propose a complementary combination of two recognizer approaches: a holistic approach and a component analysis. The holistic approach utilizes the global shape information of a character image to recognize a radical at a specific location of the character. On the contrary, the component analysis utilizes a detailed local shape of a segmented radical image to recognize the radical. The former is effective for character degradation whereas the latter is strong at processing ambiguous characters and font variations. In an evaluation of 50,000 video character images of Korean script, the proposed method achieved 96.5% accuracy. From this, we may draw a conclusion that the proposed method works well even with low quality images of complicated characters.
Affiliations:
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Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Complementary combination of holistic and component analysis for recognition of low-resolution video character images</title><author><name sortKey="Lee, Seonghun" sort="Lee, Seonghun" uniqKey="Lee S" first="Seonghun" last="Lee">Seonghun Lee</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science, KAIST, 373-1 Guseong-dong</s1><s2>Yuseong-gu, Daejeon</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Corée du Sud</country><wicri:noRegion>Yuseong-gu, Daejeon</wicri:noRegion></affiliation></author><author><name sortKey="Kim, Jinhyung" sort="Kim, Jinhyung" uniqKey="Kim J" first="Jinhyung" last="Kim">Jinhyung Kim</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science, KAIST, 373-1 Guseong-dong</s1><s2>Yuseong-gu, Daejeon</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Corée du Sud</country><wicri:noRegion>Yuseong-gu, Daejeon</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">08-0105777</idno><date when="2008">2008</date><idno type="stanalyst">PASCAL 08-0105777 INIST</idno><idno type="RBID">Pascal:08-0105777</idno><idno type="wicri:Area/PascalFrancis/Corpus">000289</idno><idno type="wicri:Area/PascalFrancis/Curation">000495</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000248</idno><idno type="wicri:doubleKey">0167-8655:2008:Lee S:complementary:combination:of</idno><idno type="wicri:Area/Main/Merge">000D38</idno><idno type="wicri:Area/Main/Curation">000D26</idno><idno type="wicri:Area/Main/Exploration">000D26</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Complementary combination of holistic and component analysis for recognition of low-resolution video character images</title><author><name sortKey="Lee, Seonghun" sort="Lee, Seonghun" uniqKey="Lee S" first="Seonghun" last="Lee">Seonghun Lee</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science, KAIST, 373-1 Guseong-dong</s1><s2>Yuseong-gu, Daejeon</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Corée du Sud</country><wicri:noRegion>Yuseong-gu, Daejeon</wicri:noRegion></affiliation></author><author><name sortKey="Kim, Jinhyung" sort="Kim, Jinhyung" uniqKey="Kim J" first="Jinhyung" last="Kim">Jinhyung Kim</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science, KAIST, 373-1 Guseong-dong</s1><s2>Yuseong-gu, Daejeon</s2><s3>KOR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Corée du Sud</country><wicri:noRegion>Yuseong-gu, Daejeon</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Pattern recognition letters</title><title level="j" type="abbreviated">Pattern recogn. lett.</title><idno type="ISSN">0167-8655</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Pattern recognition letters</title><title level="j" type="abbreviated">Pattern recogn. lett.</title><idno type="ISSN">0167-8655</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accuracy</term><term>Character recognition</term><term>Chinese</term><term>Degradation</term><term>Image processing</term><term>Image segmentation</term><term>Korean</term><term>Localization</term><term>Low resolution</term><term>Optical character recognition</term><term>Pattern recognition</term><term>Signal classification</term><term>Support vector machine</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Basse résolution</term><term>Reconnaissance optique caractère</term><term>Chinois</term><term>Coréen</term><term>Localisation</term><term>Segmentation image</term><term>Dégradation</term><term>Précision</term><term>Reconnaissance caractère</term><term>Machine vecteur support</term><term>Reconnaissance forme</term><term>Traitement image</term><term>Classification signal</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Video OCR aims at extracting text from video images in order to understand the context of the video. Video character images are usually given in low resolution with unique characteristics such as large stroke distortion, font variation, and variable size. Therefore, recognizing such characters in video images is very challenging. This is particularly true in the case of Chinese and Korean languages, where characters have complicated shapes and the number of classes (characters) is very large. In this paper, we propose a complementary combination of two recognizer approaches: a holistic approach and a component analysis. The holistic approach utilizes the global shape information of a character image to recognize a radical at a specific location of the character. On the contrary, the component analysis utilizes a detailed local shape of a segmented radical image to recognize the radical. The former is effective for character degradation whereas the latter is strong at processing ambiguous characters and font variations. In an evaluation of 50,000 video character images of Korean script, the proposed method achieved 96.5% accuracy. From this, we may draw a conclusion that the proposed method works well even with low quality images of complicated characters.</div></front></TEI><affiliations><list><country><li>Corée du Sud</li></country></list><tree><country name="Corée du Sud"><noRegion><name sortKey="Lee, Seonghun" sort="Lee, Seonghun" uniqKey="Lee S" first="Seonghun" last="Lee">Seonghun Lee</name></noRegion><name sortKey="Kim, Jinhyung" sort="Kim, Jinhyung" uniqKey="Kim J" first="Jinhyung" last="Kim">Jinhyung Kim</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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