Hybrid OCR combination approach complemented by a specialized ICR applied on ancient documents
Identifieur interne : 000203 ( France/Analysis ); précédent : 000202; suivant : 000204Hybrid OCR combination approach complemented by a specialized ICR applied on ancient documents
Auteurs : Hubert Cecotti [France] ; Abdel Belaïd [France]Source :
English descriptors
Abstract
In spite of the improvement of Commercial Optical Character Recognition (OCR) during the last years, their ability to process different kinds of documents can also be a default. They cannot produce a perfect recognition for all documents. However they allow producing high result for standard cases. We propose in this paper a model 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 of 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:
Affiliations:
- France
- Lorraine
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
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- to stream Main, to step Exploration: 001196
- to stream France, to step Extraction: 000203
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Hal:inria-00000363Le document en format XML
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They cannot produce a perfect recognition for all documents. However they allow producing high result for standard cases. We propose in this paper a model 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 of 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></TEI><affiliations><list><country><li>France</li></country><region><li>Lorraine</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Lorraine"><name sortKey="Cecotti, Hubert" sort="Cecotti, Hubert" uniqKey="Cecotti H" first="Hubert" last="Cecotti">Hubert Cecotti</name></region><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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