OcrV1, France, Analysis, bibRecord, 000204

Hybrid OCR combination for ancient documents

Identifieur interne : 000204 ( France/Analysis ); précédent : 000203; suivant : 000205

Hybrid OCR combination for ancient documents

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

Source :

RBID : Hal:inria-00000366

English descriptors

mix :
- ancient document, behavior knowledge space, character recognition, classifier combination, neural network, voting method.

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://hal.inria.fr/inria-00000366

DOI: 10.1007/11551188

Affiliations:

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

to stream Hal, to step Corpus: 000060
to stream Hal, to step Curation: 000060
to stream Hal, to step Checkpoint: 000134
to stream Main, to step Merge: 001231
to stream Main, to step Curation: 001197
to stream Main, to step Exploration: 001197
to stream France, to step Extraction: 000204

Links to Exploration step

Hal:inria-00000366

Le document en format XML

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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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Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/France/Analysis

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000204 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/France/Analysis/biblio.hfd -nk 000204 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    France
   |étape=   Analysis
   |type=    RBID
   |clé=     Hal:inria-00000366
   |texte=   Hybrid OCR combination for ancient 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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Hybrid OCR combination for ancient documents

Hybrid OCR combination for ancient documents

Source :

English descriptors

Abstract

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

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri