OcrV1, Main, Curation, bibRecord, 001294

Hybrid OCR Combination for Ancient Documents

Identifieur interne : 001294 ( Main/Curation ); précédent : 001293; suivant : 001295

Hybrid OCR Combination for Ancient Documents

Auteurs : Hubert Cecotti [France] ; Abdel Belaïd [France]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.

RBID : ISTEX:FFEAD1F3CDBEF35E2AC2B68BB2C35789522DCB30

Descripteurs français

Pascal (Inist)
- Fonction erreur, Fouille donnée, Méthode adaptative, Reconnaissance caractère, Reconnaissance forme, Reconnaissance optique caractère, Réseau neuronal, Topologie circuit.

English descriptors

KwdEn :
- Adaptive method, Character recognition, Data mining, Error function, Network topology, Neural network, Optical character recognition, Pattern recognition.

Abstract

Abstract: Commercial Optical Character Recognition (OCR) have at lot improved in the last few years. Their outstanding ability to process different kinds of documents is their main quality. However, their generality can also be an issue, as they cannot recognize perfectly documents far from the average present-day documents. We propose in this paper a system combining several OCRs and a specialized ICR (Intelligent Character Recognition) based on a convolutional neural network to complement them. Instead of just performing several OCRs in parallel and applying a fusing rule on the results, a specialized neural network with an adaptive topology is added to complement the OCRs, in function of the OCRs errors. This system has been tested on ancient documents containing old characters and old fonts not used in contemporary documents. The OCRs combination increases the recognition of about 3% whereas the ICR improves the recognition of rejected characters of more than 5%.

Url:

https://api.istex.fr/document/FFEAD1F3CDBEF35E2AC2B68BB2C35789522DCB30/fulltext/pdf

DOI: 10.1007/11551188_71

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ISTEX:FFEAD1F3CDBEF35E2AC2B68BB2C35789522DCB30

Le document en format XML

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<front><div type="abstract" xml:lang="en">Abstract: Commercial Optical Character Recognition (OCR) have at lot improved in the last few years. Their outstanding ability to process different kinds of documents is their main quality. However, their generality can also be an issue, as they cannot recognize perfectly documents far from the average present-day documents. We propose in this paper a system combining several OCRs and a specialized ICR (Intelligent Character Recognition) based on a convolutional neural network to complement them. Instead of just performing several OCRs in parallel and applying a fusing rule on the results, a specialized neural network with an adaptive topology is added to complement the OCRs, in function of the OCRs errors. This system has been tested on ancient documents containing old characters and old fonts not used in contemporary documents. The OCRs combination increases the recognition of about 3% whereas the ICR improves the recognition of rejected characters of more than 5%.</div>
</front>
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<front><div type="abstract" xml:lang="en">Commercial Optical Character Recognition (OCR) have at lot improved in the last few years. Their outstanding ability to process different kinds of documents is their main quality. However, their generality can also be an issue, as they cannot recognize perfectly documents far from the average present-day documents. We propose in this paper a system combining several OCRs and a specialized ICR (Intelligent Character Recognition) based on a convolutional neural network to complement them. Instead of just performing several OCRs in parallel and applying a fusing rule on the results, a specialized neural network with an adaptive topology is added to complement the OCRs, in function of the OCRs errors. This system has been tested on ancient documents containing old characters and old fonts not used in contemporary documents. The OCRs combination increases the recognition of about 3% whereas the ICR improves the recognition of rejected characters of more than 5%.</div>
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Hybrid OCR Combination for Ancient Documents

Hybrid OCR Combination for Ancient Documents

Source :

Descripteurs français

English descriptors

Abstract

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