InforLorV4, Main, Exploration, bibRecord, 001E91

Labelling logical structures of document images using a dynamic perceptive neural network

Identifieur interne : 001E91 ( Main/Exploration ); précédent : 001E90; suivant : 001E92

Labelling logical structures of document images using a dynamic perceptive neural network

Auteurs : Yves Rangoni [France] ; Abdet Belaïd [France] ; Szilárd Vajda [Allemagne]

Source :

International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2012.

RBID : Pascal:12-0415345

Descripteurs français

Pascal (Inist)
- Etiquetage, Système dynamique, Reconnaissance caractère, Reconnaissance optique caractère, Texte, Classification, Analyse documentaire, Analyse image, Reconnaissance image, Traitement image, Structure document, Présentation document, Perception sensorielle, Temps occupation, Taux erreur, Réseau neuronal, Modèle dynamique, Modélisation, Temps retard, Système à retard, Segmentation, ..
Wicri :
- topic : Classification.

English descriptors

KwdEn :
- Character recognition, Classification, Delay system, Delay time, Document analysis, Document layout, Document structure, Dynamic model, Dynamical system, Error rate, Image analysis, Image processing, Image recognition, Labelling, Modeling, Neural network, Occupation time, Optical character recognition, Segmentation, Sensorial perception, Text.

Abstract

This paper proposes a new method for labelling the logical structures of document images. The system starts with digitised images of paper documents, performs a physical layout analysis, runs an OCR and finally exploits the OCR's outputs to find the meaning of each block of text (i.e. assigns labels like "Title", "Author", etc.). The method is an extension of our previous work where a classifier, the perceptive neural network, has been developed to be an analogy of the human perception. We introduce in this connectionist model a temporal dimension by the use of a time-delay neural network with local representation. During the recognition stage, the system performs several recognition cycles and corrections, while keeping track and reusing the previous outputs. This dynamic classifier allows then a better handling of noise and segmentation errors. The experiments have been carried out on two datasets: the public MARG containing more than 1,500 front pages of scientific papers with four zones of interest and another one composed of documents from the Siggraph 2003 conference, where 21 logical structures have been identified. The error rate on MARG is less than 2.5% and 7.3% on the Siggraph dataset.

Affiliations:

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

to stream PascalFrancis, to step Corpus: 000100
to stream PascalFrancis, to step Curation: 000912
to stream PascalFrancis, to step Checkpoint: 000104
to stream Main, to step Merge: 001F13
to stream Main, to step Curation: 001E91

Le document en format XML

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Labelling logical structures of document images using a dynamic perceptive neural network

Labelling logical structures of document images using a dynamic perceptive neural network

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