OcrV1, PascalFrancis, Curation, bibRecord, 000948

The combination of multiple classifiers by a neural network approach

Identifieur interne : 000948 ( PascalFrancis/Curation ); précédent : 000947; suivant : 000949

The combination of multiple classifiers by a neural network approach

Auteurs : Y. S. Huang [Canada] ; K. Liu ; C. Y. Suen

Source :

International journal of pattern recognition and artificial intelligence [ 0218-0014 ] ; 1995.

RBID : Pascal:95-0511946

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Classificateur, Caractère manuscrit, Performance algorithme, Méthode itérative, Méthode gradient, Règle décision, Base donnée, Unconstrained handwriting recognition, OCR, Multi layer perceptron, Generalized delta rule.
Wicri :
- topic : Base de données.

English descriptors

KwdEn :
- Algorithm performance, Character recognition, Classifiers, Database, Decision rule, Gradient method, Iterative methods, Manuscript character.

Abstract

Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. Recently, a new trend has emerged to tackle this problem by the use of multiple classifiers. This method combines individual classification decisions to derive the final decisions. This is called "Combination of Multiple Classifiers" (CME). In this paper, a novel approach to CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. The larger a likeness measurement is, the more probable the corresponding class has the input. In data classification, neural networks have been found very suitable to aggregate the transformed output to produce the final classification decisions. Some strategies for further improving the performance of neural networks have also been proposed in this paper. Experiments with several data transformation functions and data classification approaches have been performed on a large number of handwritten samples. The best result among them is achieved by using both the proposed data transformation function and the multi-layer perceptron neural net, which increased the recognition rate of three individual classifications considerably.

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A03		`1`		`@0 Int. j. pattern recogn. artif. intell.`
A05				`@2 9`
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A08	`01`	`1`	`ENG`	`@1 The combination of multiple classifiers by a neural network approach`
A11	`01`	`1`		`@1 HUANG (Y. S.)`
A11	`02`	`1`		`@1 LIU (K.)`
A11	`03`	`1`		`@1 SUEN (C. Y.)`
A14	`01`			`@1 Concordia univ., cent. pattern recognition machine intelligence @2 Montréal PQ H3G 1M8 @3 CAN`
A20				`@1 579-597`
A21				`@1 1995`
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A64	`01`	`1`		`@0 International journal of pattern recognition and artificial intelligence`
A66	`01`			`@0 SGP`
C01	`01`		`ENG`	@0 Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. Recently, a new trend has emerged to tackle this problem by the use of multiple classifiers. This method combines individual classification decisions to derive the final decisions. This is called "Combination of Multiple Classifiers" (CME). In this paper, a novel approach to CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. The larger a likeness measurement is, the more probable the corresponding class has the input. In data classification, neural networks have been found very suitable to aggregate the transformed output to produce the final classification decisions. Some strategies for further improving the performance of neural networks have also been proposed in this paper. Experiments with several data transformation functions and data classification approaches have been performed on a large number of handwritten samples. The best result among them is achieved by using both the proposed data transformation function and the multi-layer perceptron neural net, which increased the recognition rate of three individual classifications considerably.
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C03	`01`	`1`	`FRE`	`@0 Reconnaissance caractère @3 P @5 01`
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C03	`02`	`1`	`FRE`	`@0 Classificateur @5 02`
C03	`02`	`1`	`ENG`	`@0 Classifiers @5 02`
C03	`03`	`X`	`FRE`	`@0 Caractère manuscrit @5 16`
C03	`03`	`X`	`ENG`	`@0 Manuscript character @5 16`
C03	`03`	`X`	`SPA`	`@0 Carácter manuscrito @5 16`
C03	`04`	`X`	`FRE`	`@0 Performance algorithme @5 17`
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C03	`08`	`X`	`FRE`	`@0 Base donnée @5 21`
C03	`08`	`X`	`ENG`	`@0 Database @5 21`
C03	`08`	`X`	`SPA`	`@0 Base dato @5 21`
C03	`09`	`1`	`FRE`	`@0 Unconstrained handwriting recognition @4 INC @5 72`
C03	`10`	`1`	`FRE`	`@0 OCR @4 INC @5 73`
C03	`11`	`1`	`FRE`	`@0 Multi layer perceptron @4 INC @5 74`
C03	`12`	`1`	`FRE`	`@0 Generalized delta rule @4 INC @5 75`
N21				`@1 289`

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Le document en format XML

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The combination of multiple classifiers by a neural network approach

The combination of multiple classifiers by a neural network approach

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri