OcrV1, Main, Merge, bibRecord, 001732

A Chinese OCR spelling check approach based on statistical language models

Identifieur interne : 001732 ( Main/Merge ); précédent : 001731; suivant : 001733

A Chinese OCR spelling check approach based on statistical language models

Auteurs : LI ZHUANG [République populaire de Chine] ; TA BAO [République populaire de Chine] ; XIAOYAN ZHU [République populaire de Chine] ; CHUNHENG WANG [République populaire de Chine] ; Satoshi Naoi [Japon]

Source :

RBID : Pascal:06-0112413

Descripteurs français

Pascal (Inist)
- Chinois, Reconnaissance optique caractère, Analyse langage, Reconnaissance caractère, Sémantique, Modèle statistique, Modélisation, Analyse sémantique, Représentation connaissance, ..

English descriptors

KwdEn :
- Character recognition, Chinese, Knowledge representation, Language analysis, Modeling, Optical character recognition, Semantic analysis, Semantics, Statistical model.

Abstract

This paper describes an effective spelling check approach for Chinese OCR with a new multiknowledge based statistical language model. This language model combines the conventional n-gram language model and the new LSA (Latent Semantic Analysis) language model, so both local information (syntax) and global information (semantic) are utilized. Furthermore, Chinese similar characters are used in Viterbi search process to expand the candidate list in order to add more possible correct results. With our approach, the best recognition accuracy rate increases from 79.3% to 91.9%, which means 60.9% error reduction.

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to stream PascalFrancis, to step Corpus: 000405
to stream PascalFrancis, to step Curation: 000382
to stream PascalFrancis, to step Checkpoint: 000521

Links to Exploration step

Pascal:06-0112413

Le document en format XML

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<author><name sortKey="Li Zhuang" sort="Li Zhuang" uniqKey="Li Zhuang" last="Li Zhuang">LI ZHUANG</name>
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<author><name sortKey="Chunheng Wang" sort="Chunheng Wang" uniqKey="Chunheng Wang" last="Chunheng Wang">CHUNHENG WANG</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">A Chinese OCR spelling check approach based on statistical language models</title>
<author><name sortKey="Li Zhuang" sort="Li Zhuang" uniqKey="Li Zhuang" last="Li Zhuang">LI ZHUANG</name>
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<author><name sortKey="Ta Bao" sort="Ta Bao" uniqKey="Ta Bao" last="Ta Bao">TA BAO</name>
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<term>Optical character recognition</term>
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<term>Semantics</term>
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<term>Reconnaissance caractère</term>
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<front><div type="abstract" xml:lang="en">This paper describes an effective spelling check approach for Chinese OCR with a new multiknowledge based statistical language model. This language model combines the conventional n-gram language model and the new LSA (Latent Semantic Analysis) language model, so both local information (syntax) and global information (semantic) are utilized. Furthermore, Chinese similar characters are used in Viterbi search process to expand the candidate list in order to add more possible correct results. With our approach, the best recognition accuracy rate increases from 79.3% to 91.9%, which means 60.9% error reduction.</div>
</front>
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<name sortKey="Ta Bao" sort="Ta Bao" uniqKey="Ta Bao" last="Ta Bao">TA BAO</name>
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A Chinese OCR spelling check approach based on statistical language models

A Chinese OCR spelling check approach based on statistical language models

Source :

Descripteurs français

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