OcrV1, PascalFrancis, Corpus, bibRecord, 000323

Support vector machines for mathematical symbol recognition

Identifieur interne : 000323 ( PascalFrancis/Corpus ); précédent : 000322; suivant : 000324

Support vector machines for mathematical symbol recognition

Auteurs : Christopher Malon ; Seiichi Uchida ; Masakazu Suzuki

Source :

Lecture notes in computer science [ 0302-9743 ] ; 2006.

RBID : Pascal:07-0464299

Descripteurs français

Pascal (Inist)
- Analyse syntaxique, Analyse structurale, Apprentissage probabilités, Reconnaissance forme, Reconnaissance caractère, Reconnaissance optique caractère, Caractère manuscrit, Formule mathématique, Analyse statistique, Machine exemple support.

English descriptors

KwdEn :
- Character recognition, Manuscript character, Mathematical formula, Optical character recognition, Pattern recognition, Probability learning, Statistical analysis, Structural analysis, Syntactic analysis, Vector support machine.

Abstract

Mathematical formulas challenge an OCR system with a range of similar-looking characters whose bold, calligraphic, and italic varieties must be recognized distinctly, though the fonts to be used in an article are not known in advance. We describe the use of support vector machines (SVM) to learn and predict about 300 classes of styled characters and symbols.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0302-9743`
A05				`@2 4109`
A08	`01`	`1`	`ENG`	`@1 Support vector machines for mathematical symbol recognition`
A09	`01`	`1`	`ENG`	`@1 Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006 : proceedings`
A11	`01`	`1`		`@1 MALON (Christopher)`
A11	`02`	`1`		`@1 UCHIDA (Seiichi)`
A11	`03`	`1`		`@1 SUZUKI (Masakazu)`
A14	`01`			`@1 Engineering Division, Faculty of Mathematics, Kyushu University 6-10-1 Hakozaki @2 Higashi-ku, Fukuoka, 812-8581 @3 JPN @Z 1 aut. @Z 3 aut.`
A14	`02`			`@1 Faculty of Information Science and Electrical Engineering, Kyushu University 6-10-1 Hakozaki @2 Higashi-ku, Fukuoka, 812-8581 @3 JPN @Z 2 aut.`
A18	`01`	`1`		`@1 International association for pattern recognition @3 INT @9 org-cong.`
A20				`@1 136-144`
A21				`@1 2006`
A23	`01`			`@0 ENG`
A26	`01`			`@0 3-540-37236-9`
A43	`01`			`@1 INIST @2 16343 @5 354000153609860140`
A44				`@0 0000 @1 © 2007 INIST-CNRS. All rights reserved.`
A45				`@0 8 ref.`
A47	`01`	`1`		`@0 07-0464299`
A60				`@1 P @2 C`
A61				`@0 A`
A64	`01`	`1`		`@0 Lecture notes in computer science`
A66	`01`			`@0 DEU`
C01	`01`		`ENG`	`@0 Mathematical formulas challenge an OCR system with a range of similar-looking characters whose bold, calligraphic, and italic varieties must be recognized distinctly, though the fonts to be used in an article are not known in advance. We describe the use of support vector machines (SVM) to learn and predict about 300 classes of styled characters and symbols.`
C02	`01`	`X`		`@0 001D02B07B`
C03	`01`	`X`	`FRE`	`@0 Analyse syntaxique @5 01`
C03	`01`	`X`	`ENG`	`@0 Syntactic analysis @5 01`
C03	`01`	`X`	`SPA`	`@0 Análisis sintáxico @5 01`
C03	`02`	`X`	`FRE`	`@0 Analyse structurale @5 02`
C03	`02`	`X`	`ENG`	`@0 Structural analysis @5 02`
C03	`02`	`X`	`SPA`	`@0 Análisis estructural @5 02`
C03	`03`	`X`	`FRE`	`@0 Apprentissage probabilités @5 06`
C03	`03`	`X`	`ENG`	`@0 Probability learning @5 06`
C03	`03`	`X`	`SPA`	`@0 Aprendizaje probabilidades @5 06`
C03	`04`	`X`	`FRE`	`@0 Reconnaissance forme @5 07`
C03	`04`	`X`	`ENG`	`@0 Pattern recognition @5 07`
C03	`04`	`X`	`SPA`	`@0 Reconocimiento patrón @5 07`
C03	`05`	`X`	`FRE`	`@0 Reconnaissance caractère @5 08`
C03	`05`	`X`	`ENG`	`@0 Character recognition @5 08`
C03	`05`	`X`	`SPA`	`@0 Reconocimiento carácter @5 08`
C03	`06`	`X`	`FRE`	`@0 Reconnaissance optique caractère @5 09`
C03	`06`	`X`	`ENG`	`@0 Optical character recognition @5 09`
C03	`06`	`X`	`SPA`	`@0 Reconocimento óptico de caracteres @5 09`
C03	`07`	`X`	`FRE`	`@0 Caractère manuscrit @5 10`
C03	`07`	`X`	`ENG`	`@0 Manuscript character @5 10`
C03	`07`	`X`	`SPA`	`@0 Carácter manuscrito @5 10`
C03	`08`	`X`	`FRE`	`@0 Formule mathématique @5 18`
C03	`08`	`X`	`ENG`	`@0 Mathematical formula @5 18`
C03	`08`	`X`	`SPA`	`@0 Fórmula matemática @5 18`
C03	`09`	`X`	`FRE`	`@0 Analyse statistique @5 23`
C03	`09`	`X`	`ENG`	`@0 Statistical analysis @5 23`
C03	`09`	`X`	`SPA`	`@0 Análisis estadístico @5 23`
C03	`10`	`X`	`FRE`	`@0 Machine exemple support @5 24`
C03	`10`	`X`	`ENG`	`@0 Vector support machine @5 24`
C03	`10`	`X`	`SPA`	`@0 Máquina ejemplo soporte @5 24`
N21				`@1 302`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

A30	`01`	`1`	`ENG`	`@1 International Workshop on Structural and Syntactic Pattern Recognition @2 11 @3 Hong Kong CHN @4 2006`
A30	`02`	`1`	`ENG`	`@1 International Workshop on Statistical Techniques in Pattern Recognition @2 6 @3 Hong Kong CHN @4 2006`

Format Inist (serveur)

NO :	PASCAL 07-0464299 INIST
ET :	Support vector machines for mathematical symbol recognition
AU :	MALON (Christopher); UCHIDA (Seiichi); SUZUKI (Masakazu)
AF :	Engineering Division, Faculty of Mathematics, Kyushu University 6-10-1 Hakozaki/Higashi-ku, Fukuoka, 812-8581/Japon (1 aut., 3 aut.); Faculty of Information Science and Electrical Engineering, Kyushu University 6-10-1 Hakozaki/Higashi-ku, Fukuoka, 812-8581/Japon (2 aut.)
DT :	Publication en série; Congrès; Niveau analytique
SO :	Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2006; Vol. 4109; Pp. 136-144; Bibl. 8 ref.
LA :	Anglais
EA :	Mathematical formulas challenge an OCR system with a range of similar-looking characters whose bold, calligraphic, and italic varieties must be recognized distinctly, though the fonts to be used in an article are not known in advance. We describe the use of support vector machines (SVM) to learn and predict about 300 classes of styled characters and symbols.
CC :	001D02B07B
FD :	Analyse syntaxique; Analyse structurale; Apprentissage probabilités; Reconnaissance forme; Reconnaissance caractère; Reconnaissance optique caractère; Caractère manuscrit; Formule mathématique; Analyse statistique; Machine exemple support
ED :	Syntactic analysis; Structural analysis; Probability learning; Pattern recognition; Character recognition; Optical character recognition; Manuscript character; Mathematical formula; Statistical analysis; Vector support machine
SD :	Análisis sintáxico; Análisis estructural; Aprendizaje probabilidades; Reconocimiento patrón; Reconocimiento carácter; Reconocimento óptico de caracteres; Carácter manuscrito; Fórmula matemática; Análisis estadístico; Máquina ejemplo soporte
LO :	INIST-16343.354000153609860140
ID :	07-0464299

Links to Exploration step

Pascal:07-0464299

Le document en format XML

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<ET>Support vector machines for mathematical symbol recognition</ET>
<AU>MALON (Christopher); UCHIDA (Seiichi); SUZUKI (Masakazu)</AU>
<AF>Engineering Division, Faculty of Mathematics, Kyushu University 6-10-1 Hakozaki/Higashi-ku, Fukuoka, 812-8581/Japon (1 aut., 3 aut.); Faculty of Information Science and Electrical Engineering, Kyushu University 6-10-1 Hakozaki/Higashi-ku, Fukuoka, 812-8581/Japon (2 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2006; Vol. 4109; Pp. 136-144; Bibl. 8 ref.</SO>
<LA>Anglais</LA>
<EA>Mathematical formulas challenge an OCR system with a range of similar-looking characters whose bold, calligraphic, and italic varieties must be recognized distinctly, though the fonts to be used in an article are not known in advance. We describe the use of support vector machines (SVM) to learn and predict about 300 classes of styled characters and symbols.</EA>
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<FD>Analyse syntaxique; Analyse structurale; Apprentissage probabilités; Reconnaissance forme; Reconnaissance caractère; Reconnaissance optique caractère; Caractère manuscrit; Formule mathématique; Analyse statistique; Machine exemple support</FD>
<ED>Syntactic analysis; Structural analysis; Probability learning; Pattern recognition; Character recognition; Optical character recognition; Manuscript character; Mathematical formula; Statistical analysis; Vector support machine</ED>
<SD>Análisis sintáxico; Análisis estructural; Aprendizaje probabilidades; Reconocimiento patrón; Reconocimiento carácter; Reconocimento óptico de caracteres; Carácter manuscrito; Fórmula matemática; Análisis estadístico; Máquina ejemplo soporte</SD>
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	Serveur d'exploration sur l'OCR
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Serveur d'exploration sur l'OCR

Support vector machines for mathematical symbol recognition

Support vector machines for mathematical symbol recognition

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