A robust method of recognizing multi-font rotated characters
This work presents a new robust recognition method for rotated character images. We first construct an eigen sub-space for each category using the covariance matrix calculated from a sufficient number of rotated patterns averaged by several fonts. Next, we can obtain a locus by projecting their rota...
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| creator | Hase, H. Shinokawa, T. Tokai, S. Suen, C.Y. |
| description | This work presents a new robust recognition method for rotated character images. We first construct an eigen sub-space for each category using the covariance matrix calculated from a sufficient number of rotated patterns averaged by several fonts. Next, we can obtain a locus by projecting their rotated characters onto the eigen subspace and interpolating between their projected points. An unknown character is also projected onto the eigen sub-space of each category. Then, verification is carried out by calculating the distance between the projected point of the unknown character and the locus. In our experiment, we obtained quite good results for three fonts of 26 capital letters of the English alphabet. |
| doi_str_mv | 10.1109/ICPR.2004.1334219 |
| format | Conference Proceeding |
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We first construct an eigen sub-space for each category using the covariance matrix calculated from a sufficient number of rotated patterns averaged by several fonts. Next, we can obtain a locus by projecting their rotated characters onto the eigen subspace and interpolating between their projected points. An unknown character is also projected onto the eigen sub-space of each category. Then, verification is carried out by calculating the distance between the projected point of the unknown character and the locus. 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ICPR 2004</title><addtitle>ICPR</addtitle><description>This work presents a new robust recognition method for rotated character images. We first construct an eigen sub-space for each category using the covariance matrix calculated from a sufficient number of rotated patterns averaged by several fonts. Next, we can obtain a locus by projecting their rotated characters onto the eigen subspace and interpolating between their projected points. An unknown character is also projected onto the eigen sub-space of each category. Then, verification is carried out by calculating the distance between the projected point of the unknown character and the locus. In our experiment, we obtained quite good results for three fonts of 26 capital letters of the English alphabet.</description><subject>Character recognition</subject><subject>Covariance matrix</subject><subject>Educational institutions</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Handwriting recognition</subject><subject>Image recognition</subject><subject>Information science</subject><subject>Machine intelligence</subject><subject>Pattern recognition</subject><subject>Robustness</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>0769521282</isbn><isbn>9780769521282</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tKw0AUQAcfYFv9AHGTH5h477wyF9yUYLVQUETXZTKPNtIkkkwX-vUKdnU2hwOHsVuEEhHofl2_vpUCQJUopRJIZ2wmrEReqUqfszlUhrRAYcUFmyFo5MpovGLzafoEECC1nbGHZTEOzXHKRRfzfgjFkIox-mHXtz9tvyu64yG3PA19_vOyyzEUfu9G53Mcp2t2mdxhijcnLtjH6vG9fuabl6d1vdzwFiuduQIgQkGBZBPBmgAy-WTBVTopHywo44koISoNLirtKAjTgFHGGFGRXLC7_24bY9x-jW3nxu_t6Vr-ArXfSFs</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Hase, H.</creator><creator>Shinokawa, T.</creator><creator>Tokai, S.</creator><creator>Suen, C.Y.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2004</creationdate><title>A robust method of recognizing multi-font rotated characters</title><author>Hase, H. ; Shinokawa, T. ; Tokai, S. ; Suen, C.Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-40099129d93be086d03fcf80a75f4cd8046c999f11450ae45a9d26b0646662793</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Character recognition</topic><topic>Covariance matrix</topic><topic>Educational institutions</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Handwriting recognition</topic><topic>Image recognition</topic><topic>Information science</topic><topic>Machine intelligence</topic><topic>Pattern recognition</topic><topic>Robustness</topic><toplevel>online_resources</toplevel><creatorcontrib>Hase, H.</creatorcontrib><creatorcontrib>Shinokawa, T.</creatorcontrib><creatorcontrib>Tokai, S.</creatorcontrib><creatorcontrib>Suen, C.Y.</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>Hase, H.</au><au>Shinokawa, T.</au><au>Tokai, S.</au><au>Suen, C.Y.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A robust method of recognizing multi-font rotated characters</atitle><btitle>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004</btitle><stitle>ICPR</stitle><date>2004</date><risdate>2004</risdate><volume>2</volume><spage>363</spage><epage>366 Vol.2</epage><pages>363-366 Vol.2</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>0769521282</isbn><isbn>9780769521282</isbn><abstract>This work presents a new robust recognition method for rotated character images. We first construct an eigen sub-space for each category using the covariance matrix calculated from a sufficient number of rotated patterns averaged by several fonts. Next, we can obtain a locus by projecting their rotated characters onto the eigen subspace and interpolating between their projected points. An unknown character is also projected onto the eigen sub-space of each category. Then, verification is carried out by calculating the distance between the projected point of the unknown character and the locus. In our experiment, we obtained quite good results for three fonts of 26 capital letters of the English alphabet.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2004.1334219</doi></addata></record> |
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| ispartof | Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, 2004, Vol.2, p.363-366 Vol.2 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Character recognition Covariance matrix Educational institutions Eigenvalues and eigenfunctions Handwriting recognition Image recognition Information science Machine intelligence Pattern recognition Robustness |
| title | A robust method of recognizing multi-font rotated characters |
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