OcrV1, Main, Exploration, bibRecordById, Pascal:09-0372245

Multi-font printed Mongolian document recognition system

Identifieur interne : 000A75 ( Main/Exploration ); précédent : 000A74; suivant : 000A76

Multi-font printed Mongolian document recognition system

Auteurs : LIANGRUI PENG [République populaire de Chine] ; CHANGSONG LIU [République populaire de Chine] ; XIAOQING DING [République populaire de Chine] ; HUA WANG [République populaire de Chine] ; JIANMING JIN [République populaire de Chine]

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2009.

RBID : Pascal:09-0372245

Descripteurs français

Pascal (Inist)
- Document imprimé, Chine, Bibliothèque électronique, Reconnaissance optique caractère, Jeu caractère, Système expert, Reconnaissance caractère, Système n niveaux, Classification automatique, 0130C, Traitement image, 4230V.

English descriptors

KwdEn :
- Automatic classification, Character recognition, Character sets, China, Digital libraries, Expert systems, Multilevel system, Optical character recognition, Printed document.

Abstract

Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

Affiliations:

République populaire de Chine

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

to stream PascalFrancis, to step Corpus: 000216
to stream PascalFrancis, to step Curation: 000563
to stream PascalFrancis, to step Checkpoint: 000190
to stream Main, to step Merge: 000A84
to stream Main, to step Curation: 000A75

Le document en format XML

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<front><div type="abstract" xml:lang="en">Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.</div>
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Multi-font printed Mongolian document recognition system

Multi-font printed Mongolian document recognition system

Source :

Descripteurs français

English descriptors

Abstract

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

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri