OcrV1, PascalFrancis, Corpus, bibRecord, 000552

Extraction and recognition of artificial text in multimedia documents

Identifieur interne : 000552 ( PascalFrancis/Corpus ); précédent : 000551; suivant : 000553

Extraction and recognition of artificial text in multimedia documents

Auteurs : C. Wolf ; J.-M. Jolion

Source :

Pattern analysis and applications [ 1433-7541 ] ; 2004.

RBID : Pascal:04-0205881

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Multimédia, Signal vidéo, Traitement image, Similitude, Indexation, Texte, Séquence image, Utilisation information, Recherche par contenu, Sémantique, Homme, Choix, Extraction forme.

English descriptors

KwdEn :
- Character recognition, Choice, Content-based retrieval, Human, Image processing, Image sequence, Indexing, Information use, Multimedia, Pattern extraction, Semantics, Similarity, Text, Video signal.

Abstract

The systems currently available for content-based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localise artificial text in images and videos using a measure of accumulated gradients and morphological processing. The quality of the localised text is improved by robust multiple frame integration. A new technique for the binarisation of the text boxes based on a criterion maximizing local contrast is proposed. Finally, detection and OCR results for a commercial OCR are presented, justifying the choice of the binarisation technique.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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C01	`01`		`ENG`	@0 The systems currently available for content-based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localise artificial text in images and videos using a measure of accumulated gradients and morphological processing. The quality of the localised text is improved by robust multiple frame integration. A new technique for the binarisation of the text boxes based on a criterion maximizing local contrast is proposed. Finally, detection and OCR results for a commercial OCR are presented, justifying the choice of the binarisation technique.
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Format Inist (serveur)

NO :	PASCAL 04-0205881 INIST
ET :	Extraction and recognition of artificial text in multimedia documents
AU :	WOLF (C.); JOLION (J.-M.)
AF :	Lyon Research Center for Images and Intelligent Information Systems, INSA de Lyon, Bat., Verne, 20, Av. Albert Einstein/69621 Villeurbanne/France (1 aut., 2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Pattern analysis and applications; ISSN 1433-7541; Royaume-Uni; Da. 2004; Vol. 6; No. 4; Pp. 309-326; Bibl. 34 ref.
LA :	Anglais
EA :	The systems currently available for content-based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localise artificial text in images and videos using a measure of accumulated gradients and morphological processing. The quality of the localised text is improved by robust multiple frame integration. A new technique for the binarisation of the text boxes based on a criterion maximizing local contrast is proposed. Finally, detection and OCR results for a commercial OCR are presented, justifying the choice of the binarisation technique.
CC :	001D02C02
FD :	Reconnaissance caractère; Multimédia; Signal vidéo; Traitement image; Similitude; Indexation; Texte; Séquence image; Utilisation information; Recherche par contenu; Sémantique; Homme; Choix; Extraction forme
ED :	Character recognition; Multimedia; Video signal; Image processing; Similarity; Indexing; Text; Image sequence; Information use; Content-based retrieval; Semantics; Human; Choice; Pattern extraction
SD :	Reconocimiento carácter; Multimedia; Señal video; Procesamiento imagen; Similitud; Indización; Texto; Secuencia imagen; Uso información; Semántica; Hombre; Elección; Extracción forma
LO :	INIST-26865.354000115017570050
ID :	04-0205881

Links to Exploration step

Pascal:04-0205881

Le document en format XML

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<AF>Lyon Research Center for Images and Intelligent Information Systems, INSA de Lyon, Bat., Verne, 20, Av. Albert Einstein/69621 Villeurbanne/France (1 aut., 2 aut.)</AF>
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<LA>Anglais</LA>
<EA>The systems currently available for content-based image and video retrieval work without semantic knowledge, i.e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e.g. by key word based queries. In this paper we present an algorithm to localise artificial text in images and videos using a measure of accumulated gradients and morphological processing. The quality of the localised text is improved by robust multiple frame integration. A new technique for the binarisation of the text boxes based on a criterion maximizing local contrast is proposed. Finally, detection and OCR results for a commercial OCR are presented, justifying the choice of the binarisation technique.</EA>
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{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:04-0205881
   |texte=   Extraction and recognition of artificial text in multimedia documents
}}

This area was generated with Dilib version V0.6.32.
Data generation: Sat Nov 11 16:53:45 2017. Site generation: Mon Mar 11 23:15:16 2024

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Extraction and recognition of artificial text in multimedia documents

Extraction and recognition of artificial text in multimedia documents

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

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Le document en format XML

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