Segmentation of merged characters by neural networks and shortest path
Identifieur interne : 003044 ( Main/Merge ); précédent : 003043; suivant : 003045Segmentation of merged characters by neural networks and shortest path
Auteurs : JIN WANG [États-Unis] ; J. JeanSource :
- Pattern recognition [ 0031-3203 ] ; 1994.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
A major problem with a neural network-based approach to printed character recognition is the segmentation of merged characters. A hybrid method is proposed which combines a neural network-based deferred segmentation scheme with conventional immediate segmentation techniques. In the deferred segmentation, a neural network is employed to distinguish single characters from composites. To find a proper vertical cut that separates a composite, a shortest-path algorithm seeking minimal-penalty curved cuts is used. Integrating those components with a multiresolution neural network OCR and an efficient spelling checker, the resulting system significantly improves its ability to read omnifont document text
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000A98
- to stream PascalFrancis, to step Curation: 000902
- to stream PascalFrancis, to step Checkpoint: 000A50
Links to Exploration step
Pascal:94-0585775Le document en format XML
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<author><name sortKey="Jin Wang" sort="Jin Wang" uniqKey="Jin Wang" last="Jin Wang">JIN WANG</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Wright State univ., dep. computer sci. eng.</s1>
<s2>Dayton OH 45435</s2>
<s3>USA</s3>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Segmentation of merged characters by neural networks and shortest path</title>
<author><name sortKey="Jin Wang" sort="Jin Wang" uniqKey="Jin Wang" last="Jin Wang">JIN WANG</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Wright State univ., dep. computer sci. eng.</s1>
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<series><title level="j" type="main">Pattern recognition</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Character recognition</term>
<term>Document processing</term>
<term>Neural network</term>
<term>Pattern recognition</term>
<term>Segmentation</term>
<term>Shortest path</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Chemin plus court</term>
<term>Segmentation</term>
<term>Réseau neuronal</term>
<term>Reconnaissance caractère</term>
<term>Reconnaissance forme</term>
<term>Traitement document</term>
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<front><div type="abstract" xml:lang="en">A major problem with a neural network-based approach to printed character recognition is the segmentation of merged characters. A hybrid method is proposed which combines a neural network-based deferred segmentation scheme with conventional immediate segmentation techniques. In the deferred segmentation, a neural network is employed to distinguish single characters from composites. To find a proper vertical cut that separates a composite, a shortest-path algorithm seeking minimal-penalty curved cuts is used. Integrating those components with a multiresolution neural network OCR and an efficient spelling checker, the resulting system significantly improves its ability to read omnifont document text</div>
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