OcrV1, Main, Exploration, bibRecord, 000121

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Identifieur interne : 000121 ( Main/Exploration ); précédent : 000120; suivant : 000122

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Auteurs : Khaoula Elagouni [France] ; Christophe Garcia [France] ; Franck Mamalet [France] ; Pascale Sebillot [France]

Source :

International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2014.

RBID : Pascal:14-0199549

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Texte, Reconnaissance forme, Analyse documentaire, Multimédia, Reconnaissance optique caractère, Accès contenu, Vision ordinateur, Linguistique mathématique, Signal vidéo, Analyse scène, Présentation document, Sémantique, Balayage, Sous titrage, Convolution, Réseau neuronal, Segmentation image, Scène naturelle.
Wicri :
- topic : Multimédia.

English descriptors

KwdEn :
- Caption, Character recognition, Computational linguistics, Computer vision, Content access, Convolution, Document analysis, Document layout, Image segmentation, Multimedia, Natural scenes, Neural network, Optical character recognition, Pattern recognition, Scanning, Scene analysis, Semantics, Text, Video signal.

Abstract

Text embedded in multimedia documents represents an important semantic information that helps to automatically access the content. This paper proposes two neural-based optical character recognition (OCR) systems that handle the text recognition problem in different ways. The first approach segments a text image into individual characters before recognizing them, while the second one avoids the segmentation step by integrating a multi-scale scanning scheme that allows to jointly localize and recognize characters at each position and scale. Some linguistic knowledge is also incorporated into the proposed schemes to remove errors due to recognition confusions. Both OCR systems are applied to caption texts embedded in videos and in natural scene images and provide outstanding results showing that the proposed approaches outperform the state-of-the-art methods.

Affiliations:

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

to stream PascalFrancis, to step Corpus: 000009
to stream PascalFrancis, to step Curation: 000756
to stream PascalFrancis, to step Checkpoint: 000004
to stream Main, to step Merge: 000122
to stream Main, to step Curation: 000121

Le document en format XML

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<front><div type="abstract" xml:lang="en">Text embedded in multimedia documents represents an important semantic information that helps to automatically access the content. This paper proposes two neural-based optical character recognition (OCR) systems that handle the text recognition problem in different ways. The first approach segments a text image into individual characters before recognizing them, while the second one avoids the segmentation step by integrating a multi-scale scanning scheme that allows to jointly localize and recognize characters at each position and scale. Some linguistic knowledge is also incorporated into the proposed schemes to remove errors due to recognition confusions. Both OCR systems are applied to caption texts embedded in videos and in natural scene images and provide outstanding results showing that the proposed approaches outperform the state-of-the-art methods.</div>
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Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation

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