OcrV1, PascalFrancis, Curation, bibRecord, 000495

Complementary combination of holistic and component analysis for recognition of low-resolution video character images

Identifieur interne : 000495 ( PascalFrancis/Curation ); précédent : 000494; suivant : 000496

Complementary combination of holistic and component analysis for recognition of low-resolution video character images

Auteurs : Seonghun Lee [Corée du Sud] ; Jinhyung Kim [Corée du Sud]

Source :

Pattern recognition letters [ 0167-8655 ] ; 2008.

RBID : Pascal:08-0105777

Descripteurs français

Pascal (Inist)
- Basse résolution, Reconnaissance optique caractère, Chinois, Coréen, Localisation, Segmentation image, Dégradation, Précision, Reconnaissance caractère, Machine vecteur support, Reconnaissance forme, Traitement image, Classification signal.

English descriptors

KwdEn :
- Accuracy, Character recognition, Chinese, Degradation, Image processing, Image segmentation, Korean, Localization, Low resolution, Optical character recognition, Pattern recognition, Signal classification, Support vector machine.

Abstract

Video OCR aims at extracting text from video images in order to understand the context of the video. Video character images are usually given in low resolution with unique characteristics such as large stroke distortion, font variation, and variable size. Therefore, recognizing such characters in video images is very challenging. This is particularly true in the case of Chinese and Korean languages, where characters have complicated shapes and the number of classes (characters) is very large. In this paper, we propose a complementary combination of two recognizer approaches: a holistic approach and a component analysis. The holistic approach utilizes the global shape information of a character image to recognize a radical at a specific location of the character. On the contrary, the component analysis utilizes a detailed local shape of a segmented radical image to recognize the radical. The former is effective for character degradation whereas the latter is strong at processing ambiguous characters and font variations. In an evaluation of 50,000 video character images of Korean script, the proposed method achieved 96.5% accuracy. From this, we may draw a conclusion that the proposed method works well even with low quality images of complicated characters.

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A08	`01`	`1`	`ENG`	`@1 Complementary combination of holistic and component analysis for recognition of low-resolution video character images`
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C03	`05`	`X`	`SPA`	`@0 Localización @5 05`
C03	`06`	`3`	`FRE`	`@0 Segmentation image @5 06`
C03	`06`	`3`	`ENG`	`@0 Image segmentation @5 06`
C03	`07`	`X`	`FRE`	`@0 Dégradation @5 07`
C03	`07`	`X`	`ENG`	`@0 Degradation @5 07`
C03	`07`	`X`	`SPA`	`@0 Degradación @5 07`
C03	`08`	`X`	`FRE`	`@0 Précision @5 08`
C03	`08`	`X`	`ENG`	`@0 Accuracy @5 08`
C03	`08`	`X`	`SPA`	`@0 Precisión @5 08`
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C03	`09`	`X`	`ENG`	`@0 Character recognition @5 09`
C03	`09`	`X`	`SPA`	`@0 Reconocimiento carácter @5 09`
C03	`10`	`X`	`FRE`	`@0 Machine vecteur support @5 10`
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Le document en format XML

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Complementary combination of holistic and component analysis for recognition of low-resolution video character images

Complementary combination of holistic and component analysis for recognition of low-resolution video character images

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Descripteurs français

English descriptors

Abstract

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