OcrV1, PascalFrancis, Corpus, bibRecord, 000685

Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context

Identifieur interne : 000685 ( PascalFrancis/Corpus ); précédent : 000684; suivant : 000686

Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context

Auteurs : A. Kacem ; A. Belaïd ; M. Ben Ahmed

Source :

International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2001.

RBID : Pascal:02-0104694

Descripteurs français

Pascal (Inist)
- Méthode heuristique, Segmentation, Caractère imprimé, Reconnaissance caractère, Logique floue, Logique mathématique, Formule mathématique.

English descriptors

KwdEn :
- Character recognition, Fuzzy logic, Heuristic method, Mathematical formula, Mathematical logic, Printed character, Segmentation.

Abstract

This paper describes a new method to segment printed mathematical documents precisely and extract formulas automatically from their images. Unlike classical methods, it is more directed towards segmentation rather than recognition, isolating mathematical formulas outside and inside text-lines. Our ultimate goal is to delimit parts of text that could disturb OCR applications, not yet trained for formula recognition and restructuring. The method is based on a global and a local segmentation. The global segmentation separates isolated formulas from the text lines using a primary labeling. The local segmentation propagates the context around the mathematical operators met to discard embedded formulas from plain text. The primary labeling identifies some mathematical symbols by models created at a learning step using fuzzy logic. The secondary labeling reinforces the results of the primary labeling and locates the subscripts and the superscripts inside the text. A heuristic has been defined that guides this automatic process. In this paper, the different modules making up the automated segmentation of mathematical document system are presented with examples of results. Experiments carried out on some commonly seen mathematical documents show that our proposed method can achieve quite satisfactory rates, making mathematical formula extraction more feasible for real-world applications. The average rate of primary labeling of mathematical operators is about 95.3% and their secondary labeling can improve the rate by about 4%. The formula extraction rate, evaluated with 300 formulas and 100 mathematical documents having variable complexity, is close to 93%.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A03		`1`		`@0 Int. j. doc. anal. recognit. : (Print)`
A05				`@2 4`
A06				`@2 2`
A08	`01`	`1`	`ENG`	`@1 Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context`
A11	`01`	`1`		`@1 KACEM (A.)`
A11	`02`	`1`		`@1 BELAÏD (A.)`
A11	`03`	`1`		`@1 BEN AHMED (M.)`
A14	`01`			`@1 ENSI-RIADI, 77 rue de Carthage, Cité Mohamed Ali @2 2040 Radès @3 TUN @Z 1 aut.`
A14	`02`			`@1 LORIA-CNRS, Bâtiment LORIA, compus scientifique BP 239 @2 54506 Vandoeuvre Nancy @3 FRA @Z 2 aut.`
A14	`03`			`@1 RIADI-Tunisia @3 TUN @Z 3 aut.`
A20				`@1 97-108`
A21				`@1 2001`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 26790 @5 354000099533410030`
A44				`@0 0000 @1 © 2002 INIST-CNRS. All rights reserved.`
A45				`@0 20 ref.`
A47	`01`	`1`		`@0 02-0104694`
A60				`@1 P`
A61				`@0 A`
A64	`01`	`1`		`@0 International journal on document analysis and recognition : (Print)`
A66	`01`			`@0 DEU`
C01	`01`		`ENG`	@0 This paper describes a new method to segment printed mathematical documents precisely and extract formulas automatically from their images. Unlike classical methods, it is more directed towards segmentation rather than recognition, isolating mathematical formulas outside and inside text-lines. Our ultimate goal is to delimit parts of text that could disturb OCR applications, not yet trained for formula recognition and restructuring. The method is based on a global and a local segmentation. The global segmentation separates isolated formulas from the text lines using a primary labeling. The local segmentation propagates the context around the mathematical operators met to discard embedded formulas from plain text. The primary labeling identifies some mathematical symbols by models created at a learning step using fuzzy logic. The secondary labeling reinforces the results of the primary labeling and locates the subscripts and the superscripts inside the text. A heuristic has been defined that guides this automatic process. In this paper, the different modules making up the automated segmentation of mathematical document system are presented with examples of results. Experiments carried out on some commonly seen mathematical documents show that our proposed method can achieve quite satisfactory rates, making mathematical formula extraction more feasible for real-world applications. The average rate of primary labeling of mathematical operators is about 95.3% and their secondary labeling can improve the rate by about 4%. The formula extraction rate, evaluated with 300 formulas and 100 mathematical documents having variable complexity, is close to 93%.
C02	`01`	`X`		`@0 001D02C03`
C03	`01`	`X`	`FRE`	`@0 Méthode heuristique @5 01`
C03	`01`	`X`	`ENG`	`@0 Heuristic method @5 01`
C03	`01`	`X`	`SPA`	`@0 Método heurístico @5 01`
C03	`02`	`X`	`FRE`	`@0 Segmentation @5 02`
C03	`02`	`X`	`ENG`	`@0 Segmentation @5 02`
C03	`02`	`X`	`SPA`	`@0 Segmentación @5 02`
C03	`03`	`X`	`FRE`	`@0 Caractère imprimé @5 03`
C03	`03`	`X`	`ENG`	`@0 Printed character @5 03`
C03	`03`	`X`	`SPA`	`@0 Carácter impreso @5 03`
C03	`04`	`X`	`FRE`	`@0 Reconnaissance caractère @5 04`
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C03	`04`	`X`	`SPA`	`@0 Reconocimiento carácter @5 04`
C03	`05`	`X`	`FRE`	`@0 Logique floue @5 05`
C03	`05`	`X`	`ENG`	`@0 Fuzzy logic @5 05`
C03	`05`	`X`	`SPA`	`@0 Lógica difusa @5 05`
C03	`06`	`X`	`FRE`	`@0 Logique mathématique @5 06`
C03	`06`	`X`	`ENG`	`@0 Mathematical logic @5 06`
C03	`06`	`X`	`SPA`	`@0 Lógica matemática @5 06`
C03	`07`	`X`	`FRE`	`@0 Formule mathématique @5 07`
C03	`07`	`X`	`ENG`	`@0 Mathematical formula @5 07`
C03	`07`	`X`	`SPA`	`@0 Fórmula matemática @5 07`
N21				`@1 056`
N82				`@1 PSI`

Format Inist (serveur)

NO :	PASCAL 02-0104694 INIST
ET :	Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context
AU :	KACEM (A.); BELAÏD (A.); BEN AHMED (M.)
AF :	ENSI-RIADI, 77 rue de Carthage, Cité Mohamed Ali/2040 Radès/Tunisie (1 aut.); LORIA-CNRS, Bâtiment LORIA, compus scientifique BP 239/54506 Vandoeuvre Nancy/France (2 aut.); RIADI-Tunisia/Tunisie (3 aut.)
DT :	Publication en série; Niveau analytique
SO :	International journal on document analysis and recognition : (Print); ISSN 1433-2833; Allemagne; Da. 2001; Vol. 4; No. 2; Pp. 97-108; Bibl. 20 ref.
LA :	Anglais
EA :	This paper describes a new method to segment printed mathematical documents precisely and extract formulas automatically from their images. Unlike classical methods, it is more directed towards segmentation rather than recognition, isolating mathematical formulas outside and inside text-lines. Our ultimate goal is to delimit parts of text that could disturb OCR applications, not yet trained for formula recognition and restructuring. The method is based on a global and a local segmentation. The global segmentation separates isolated formulas from the text lines using a primary labeling. The local segmentation propagates the context around the mathematical operators met to discard embedded formulas from plain text. The primary labeling identifies some mathematical symbols by models created at a learning step using fuzzy logic. The secondary labeling reinforces the results of the primary labeling and locates the subscripts and the superscripts inside the text. A heuristic has been defined that guides this automatic process. In this paper, the different modules making up the automated segmentation of mathematical document system are presented with examples of results. Experiments carried out on some commonly seen mathematical documents show that our proposed method can achieve quite satisfactory rates, making mathematical formula extraction more feasible for real-world applications. The average rate of primary labeling of mathematical operators is about 95.3% and their secondary labeling can improve the rate by about 4%. The formula extraction rate, evaluated with 300 formulas and 100 mathematical documents having variable complexity, is close to 93%.
CC :	001D02C03
FD :	Méthode heuristique; Segmentation; Caractère imprimé; Reconnaissance caractère; Logique floue; Logique mathématique; Formule mathématique
ED :	Heuristic method; Segmentation; Printed character; Character recognition; Fuzzy logic; Mathematical logic; Mathematical formula
SD :	Método heurístico; Segmentación; Carácter impreso; Reconocimiento carácter; Lógica difusa; Lógica matemática; Fórmula matemática
LO :	INIST-26790.354000099533410030
ID :	02-0104694

Links to Exploration step

Pascal:02-0104694

Le document en format XML

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<ET>Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context</ET>
<AU>KACEM (A.); BELAÏD (A.); BEN AHMED (M.)</AU>
<AF>ENSI-RIADI, 77 rue de Carthage, Cité Mohamed Ali/2040 Radès/Tunisie (1 aut.); LORIA-CNRS, Bâtiment LORIA, compus scientifique BP 239/54506 Vandoeuvre Nancy/France (2 aut.); RIADI-Tunisia/Tunisie (3 aut.)</AF>
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<EA>This paper describes a new method to segment printed mathematical documents precisely and extract formulas automatically from their images. Unlike classical methods, it is more directed towards segmentation rather than recognition, isolating mathematical formulas outside and inside text-lines. Our ultimate goal is to delimit parts of text that could disturb OCR applications, not yet trained for formula recognition and restructuring. The method is based on a global and a local segmentation. The global segmentation separates isolated formulas from the text lines using a primary labeling. The local segmentation propagates the context around the mathematical operators met to discard embedded formulas from plain text. The primary labeling identifies some mathematical symbols by models created at a learning step using fuzzy logic. The secondary labeling reinforces the results of the primary labeling and locates the subscripts and the superscripts inside the text. A heuristic has been defined that guides this automatic process. In this paper, the different modules making up the automated segmentation of mathematical document system are presented with examples of results. Experiments carried out on some commonly seen mathematical documents show that our proposed method can achieve quite satisfactory rates, making mathematical formula extraction more feasible for real-world applications. The average rate of primary labeling of mathematical operators is about 95.3% and their secondary labeling can improve the rate by about 4%. The formula extraction rate, evaluated with 300 formulas and 100 mathematical documents having variable complexity, is close to 93%.</EA>
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Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context

Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context

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Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

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