OcrV1, Main, Merge, bibRecord, 002D52

Off-line cursive handwriting recognition using hidden Markov models

Identifieur interne : 002D52 ( Main/Merge ); précédent : 002D51; suivant : 002D53

Off-line cursive handwriting recognition using hidden Markov models

Auteurs : H. Bunke [Suisse] ; M. Roth [Suisse] ; E. G. Schukat-Talamazzini

Source :

Pattern recognition [ 0031-3203 ] ; 1995.

RBID : Pascal:95-0540564

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Caractère manuscrit, Alphabet, Modèle Markov, Cursive script recognition, Off line recognition, Hidden Markov model, Skeleton graph, OCR.

English descriptors

KwdEn :
- Alphabet, Character recognition, Manuscript character, Markov model, OCR.

Abstract

A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. Given the sequence of edges extracted from the skeleton graph, each edge is transformed into a 10-dimensional feature vector. The features represent information about the location of an edge relative to the four reference lines, its curvature and the degree of the nodes incident to the considered edge. The linear model was adopted as basic HMM topology. Each letter of the alphabet is represented by a linear HMM. Given a dictionary of fixed size, an HMM for each dictionary word is built by sequential concatenation of the HMMs representing the individual letters of the word. Training of the HMMs is done by means of the Baum-Welch algorithm, while the Viterbi algorithm is used for recognition. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.

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Pascal:95-0540564

Le document en format XML

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<author><name sortKey="Bunke, H" sort="Bunke, H" uniqKey="Bunke H" first="H." last="Bunke">H. Bunke</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Univ. Bern, Inst. Informatik angewandte Mathematik</s1>
<s2>3012 Bern</s2>
<s3>CHE</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
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<country>Suisse</country>
<placeName><settlement type="city">Berne</settlement>
<region type="région" nuts="3">Canton de Berne</region>
</placeName>
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</author>
<author><name sortKey="Roth, M" sort="Roth, M" uniqKey="Roth M" first="M." last="Roth">M. Roth</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Univ. Bern, Inst. Informatik angewandte Mathematik</s1>
<s2>3012 Bern</s2>
<s3>CHE</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
<country>Suisse</country>
<placeName><settlement type="city">Berne</settlement>
<region type="région" nuts="3">Canton de Berne</region>
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</affiliation>
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<author><name sortKey="Schukat Talamazzini, E G" sort="Schukat Talamazzini, E G" uniqKey="Schukat Talamazzini E" first="E. G." last="Schukat-Talamazzini">E. G. Schukat-Talamazzini</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Off-line cursive handwriting recognition using hidden Markov models</title>
<author><name sortKey="Bunke, H" sort="Bunke, H" uniqKey="Bunke H" first="H." last="Bunke">H. Bunke</name>
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<s2>3012 Bern</s2>
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<sZ>2 aut.</sZ>
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<author><name sortKey="Roth, M" sort="Roth, M" uniqKey="Roth M" first="M." last="Roth">M. Roth</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Univ. Bern, Inst. Informatik angewandte Mathematik</s1>
<s2>3012 Bern</s2>
<s3>CHE</s3>
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<author><name sortKey="Schukat Talamazzini, E G" sort="Schukat Talamazzini, E G" uniqKey="Schukat Talamazzini E" first="E. G." last="Schukat-Talamazzini">E. G. Schukat-Talamazzini</name>
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<series><title level="j" type="main">Pattern recognition</title>
<title level="j" type="abbreviated">Pattern recogn.</title>
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<seriesStmt><title level="j" type="main">Pattern recognition</title>
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<idno type="ISSN">0031-3203</idno>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Alphabet</term>
<term>Character recognition</term>
<term>Manuscript character</term>
<term>Markov model</term>
<term>OCR</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Reconnaissance caractère</term>
<term>Caractère manuscrit</term>
<term>Alphabet</term>
<term>Modèle Markov</term>
<term>Cursive script recognition</term>
<term>Off line recognition</term>
<term>Hidden Markov model</term>
<term>Skeleton graph</term>
<term>OCR</term>
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<front><div type="abstract" xml:lang="en">A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. Given the sequence of edges extracted from the skeleton graph, each edge is transformed into a 10-dimensional feature vector. The features represent information about the location of an edge relative to the four reference lines, its curvature and the degree of the nodes incident to the considered edge. The linear model was adopted as basic HMM topology. Each letter of the alphabet is represented by a linear HMM. Given a dictionary of fixed size, an HMM for each dictionary word is built by sequential concatenation of the HMMs representing the individual letters of the word. Training of the HMMs is done by means of the Baum-Welch algorithm, while the Viterbi algorithm is used for recognition. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.</div>
</front>
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<tree><noCountry><name sortKey="Schukat Talamazzini, E G" sort="Schukat Talamazzini, E G" uniqKey="Schukat Talamazzini E" first="E. G." last="Schukat-Talamazzini">E. G. Schukat-Talamazzini</name>
</noCountry>
<country name="Suisse"><region name="Canton de Berne"><name sortKey="Bunke, H" sort="Bunke, H" uniqKey="Bunke H" first="H." last="Bunke">H. Bunke</name>
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<name sortKey="Roth, M" sort="Roth, M" uniqKey="Roth M" first="M." last="Roth">M. Roth</name>
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Off-line cursive handwriting recognition using hidden Markov models

Off-line cursive handwriting recognition using hidden Markov 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