Robust character recognition using adaptive feature extraction
This paper describes an adaptive feature extraction method that exploits category specific information to overcome both image degradation and deformation. When recognizing multiple fonts, geometric features such as directional information of strokes are often used but they are weak against the defor...
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| creator | Mori, M. Sawaki, M. Yamato, J. |
| description | This paper describes an adaptive feature extraction method that exploits category specific information to overcome both image degradation and deformation. When recognizing multiple fonts, geometric features such as directional information of strokes are often used but they are weak against the deformation and degradation that appear in videos and natural scenes. To tackle these problems, the proposed method estimates the degree of deformation and degradation of an input pattern by comparing the input pattern and the template of each category as category specific information. This estimation enables us to compensate the aspect ratio associated with shape and the degradation in feature values. Recognition experiments using characters extracted from videos show that the proposed method is superior to the conventional alternatives in resisting deformation and degradation. |
| doi_str_mv | 10.1109/IVCNZ.2008.4762107 |
| format | Conference Proceeding |
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When recognizing multiple fonts, geometric features such as directional information of strokes are often used but they are weak against the deformation and degradation that appear in videos and natural scenes. To tackle these problems, the proposed method estimates the degree of deformation and degradation of an input pattern by comparing the input pattern and the template of each category as category specific information. This estimation enables us to compensate the aspect ratio associated with shape and the degradation in feature values. Recognition experiments using characters extracted from videos show that the proposed method is superior to the conventional alternatives in resisting deformation and degradation.</description><identifier>ISSN: 2151-2191</identifier><identifier>ISBN: 1424425824</identifier><identifier>ISBN: 1424437806</identifier><identifier>ISBN: 9781424437801</identifier><identifier>ISBN: 9781424425822</identifier><identifier>EISSN: 2151-2205</identifier><identifier>EISBN: 1424425832</identifier><identifier>EISBN: 9781424425839</identifier><identifier>DOI: 10.1109/IVCNZ.2008.4762107</identifier><identifier>LCCN: 2008904858</identifier><language>eng</language><publisher>IEEE</publisher><subject>Background noise ; category-dependent ; Character recognition ; compensation ; Data mining ; Degradation ; Feature extraction ; Layout ; OCR ; Robustness ; Shape ; Text recognition ; Videos</subject><ispartof>2008 23rd International Conference Image and Vision Computing New Zealand, 2008, p.1-6</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4762107$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4762107$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Mori, M.</creatorcontrib><creatorcontrib>Sawaki, M.</creatorcontrib><creatorcontrib>Yamato, J.</creatorcontrib><title>Robust character recognition using adaptive feature extraction</title><title>2008 23rd International Conference Image and Vision Computing New Zealand</title><addtitle>IVCNZ</addtitle><description>This paper describes an adaptive feature extraction method that exploits category specific information to overcome both image degradation and deformation. When recognizing multiple fonts, geometric features such as directional information of strokes are often used but they are weak against the deformation and degradation that appear in videos and natural scenes. To tackle these problems, the proposed method estimates the degree of deformation and degradation of an input pattern by comparing the input pattern and the template of each category as category specific information. This estimation enables us to compensate the aspect ratio associated with shape and the degradation in feature values. Recognition experiments using characters extracted from videos show that the proposed method is superior to the conventional alternatives in resisting deformation and degradation.</description><subject>Background noise</subject><subject>category-dependent</subject><subject>Character recognition</subject><subject>compensation</subject><subject>Data mining</subject><subject>Degradation</subject><subject>Feature extraction</subject><subject>Layout</subject><subject>OCR</subject><subject>Robustness</subject><subject>Shape</subject><subject>Text recognition</subject><subject>Videos</subject><issn>2151-2191</issn><issn>2151-2205</issn><isbn>1424425824</isbn><isbn>1424437806</isbn><isbn>9781424437801</isbn><isbn>9781424425822</isbn><isbn>1424425832</isbn><isbn>9781424425839</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkM1Kw0AUhcefgm3tC-gmL5B4752ZZGYjSKlaKApSXLgpk-SmjmhSkono25tixdVZnI8PzhHiAiFBBHu1fJ4_vCQEYBKVpYSQHYkJKlKKtJF0LMaEGmMi0Cf_BanTvwItjsRkL7CgjDZnYtZ1bwCAZFIyeiyun5q870JUvLrWFYHbqOWi2dY--KaO-s7X28iVbhf8J0cVu9C3HPFX2MMDcS5GlXvveHbIqVjfLtbz-3j1eLec36xibyHE1hGSQlWkUjoNXLG0lrIiK7WpQHOpyixPZSoVDYiBTLvcaaMQymE0sJyKy1-tZ-bNrvUfrv3eHD6RP16KTqQ</recordid><startdate>200811</startdate><enddate>200811</enddate><creator>Mori, M.</creator><creator>Sawaki, M.</creator><creator>Yamato, J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200811</creationdate><title>Robust character recognition using adaptive feature extraction</title><author>Mori, M. ; Sawaki, M. ; Yamato, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-9a212414c633a50efe39927c7d58f05ed4d7b636342c638075aba58410d2100e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Background noise</topic><topic>category-dependent</topic><topic>Character recognition</topic><topic>compensation</topic><topic>Data mining</topic><topic>Degradation</topic><topic>Feature extraction</topic><topic>Layout</topic><topic>OCR</topic><topic>Robustness</topic><topic>Shape</topic><topic>Text recognition</topic><topic>Videos</topic><toplevel>online_resources</toplevel><creatorcontrib>Mori, M.</creatorcontrib><creatorcontrib>Sawaki, M.</creatorcontrib><creatorcontrib>Yamato, J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mori, M.</au><au>Sawaki, M.</au><au>Yamato, J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Robust character recognition using adaptive feature extraction</atitle><btitle>2008 23rd International Conference Image and Vision Computing New Zealand</btitle><stitle>IVCNZ</stitle><date>2008-11</date><risdate>2008</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>2151-2191</issn><eissn>2151-2205</eissn><isbn>1424425824</isbn><isbn>1424437806</isbn><isbn>9781424437801</isbn><isbn>9781424425822</isbn><eisbn>1424425832</eisbn><eisbn>9781424425839</eisbn><abstract>This paper describes an adaptive feature extraction method that exploits category specific information to overcome both image degradation and deformation. When recognizing multiple fonts, geometric features such as directional information of strokes are often used but they are weak against the deformation and degradation that appear in videos and natural scenes. To tackle these problems, the proposed method estimates the degree of deformation and degradation of an input pattern by comparing the input pattern and the template of each category as category specific information. This estimation enables us to compensate the aspect ratio associated with shape and the degradation in feature values. Recognition experiments using characters extracted from videos show that the proposed method is superior to the conventional alternatives in resisting deformation and degradation.</abstract><pub>IEEE</pub><doi>10.1109/IVCNZ.2008.4762107</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 2151-2191 |
| ispartof | 2008 23rd International Conference Image and Vision Computing New Zealand, 2008, p.1-6 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Background noise category-dependent Character recognition compensation Data mining Degradation Feature extraction Layout OCR Robustness Shape Text recognition Videos |
| title | Robust character recognition using adaptive feature extraction |
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