Hybrid OCR combination for ancient documents
Identifieur interne :
000449 ( PascalFrancis/Corpus );
précédent :
000448;
suivant :
000450
Hybrid OCR combination for ancient documents
Auteurs : Hubert Cecotti ;
Abdel BelaidSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2005.
RBID : Pascal:05-0391622
Descripteurs français
English descriptors
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%.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A09 | 01 | 1 | ENG | @1 Pattern recognition and data mining : Bath, 22-25 august 2005 |
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| A11 | 01 | 1 | | @1 CECOTTI (Hubert) |
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| A12 | 01 | 1 | | @1 SINGH (Sameer) @9 ed. |
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Format Inist (serveur)
| NO : | PASCAL 05-0391622 INIST |
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| ET : | Hybrid OCR combination for ancient documents |
|---|
| AU : | CECOTTI (Hubert); BELAID (Abdel); SINGH (Sameer); SINGH (Maneesha); APTE (Chid); PERNER (Petra) |
|---|
| AF : | READ Group, LORIA/CNRS, Campus Scientifique BP 239/54506 Vandoeuvre-les-Nancy/France (1 aut., 2 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2005; Vol. 3686; Part I, 646-653; Bibl. 16 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | 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%. |
|---|
| CC : | 001D02B07B |
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| FD : | Fouille donnée; Reconnaissance caractère; Reconnaissance optique caractère; Reconnaissance forme; Topologie circuit; Réseau neuronal; Méthode adaptative; Fonction erreur |
|---|
| ED : | Data mining; Character recognition; Optical character recognition; Pattern recognition; Network topology; Neural network; Adaptive method; Error function |
|---|
| SD : | Busca dato; Reconocimiento carácter; Reconocimento óptico de caracteres; Reconocimiento patrón; Red neuronal; Método adaptativo; Función error |
|---|
| LO : | INIST-16343.354000124412760710 |
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| ID : | 05-0391622 |
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Links to Exploration step
Pascal:05-0391622
Le document en format XML
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<EA>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%.</EA>
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