OcrV1, PascalFrancis, Curation, bibRecord, 000567

Phrase-based correction model for improving handwriting recognition accuracies

Identifieur interne : 000567 ( PascalFrancis/Curation ); précédent : 000566; suivant : 000568

Phrase-based correction model for improving handwriting recognition accuracies

Auteurs : Faisal Farooq [États-Unis] ; Damien Jose [États-Unis] ; Venu Govindaraju [États-Unis]

Source :

Pattern recognition [ 0031-3203 ] ; 2009.

RBID : Pascal:09-0430753

Descripteurs français

Pascal (Inist)
- Reconnaissance écriture, Précision, Reconnaissance parole, Caractère manuscrit, Algorithme, Estimation canal, Reconnaissance optique caractère, Canal avec bruit, Correction erreur, Décodage Viterbi, Reconnaissance forme, Traitement parole, Estimation paramètre.

English descriptors

KwdEn :
- Accuracy, Algorithm, Channel estimation, Error correction, Handwriting recognition, Manuscript character, Noisy channel, Optical character recognition, Parameter estimation, Pattern recognition, Speech processing, Speech recognition, Viterbi decoding.

Abstract

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a "phrase-based" system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.

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A09	`01`	`1`	`ENG`	`@1 New Frontiers in Handwriting Recognition`
A11	`01`	`1`		`@1 FAROOQ (Faisal)`
A11	`02`	`1`		`@1 JOSE (Damien)`
A11	`03`	`1`		`@1 GOVINDARAJU (Venu)`
A12	`01`	`1`		`@1 CHERIET (Mohamed) @9 ed.`
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A12	`04`	`1`		`@1 KIMURA (Fumitaka) @9 ed.`
A12	`05`	`1`		`@1 SUEN (Ching Y.) @9 ed.`
A14	`01`			`@1 Center for Unified Biometrics and Sensors, State University of New York at Buffalo @2 Amherst, NY 14228 @3 USA @Z 1 aut. @Z 2 aut. @Z 3 aut.`
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C03	`06`	`X`	`ENG`	`@0 Channel estimation @5 06`
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C03	`07`	`X`	`FRE`	`@0 Reconnaissance optique caractère @5 07`
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C03	`08`	`X`	`FRE`	`@0 Canal avec bruit @5 08`
C03	`08`	`X`	`ENG`	`@0 Noisy channel @5 08`
C03	`08`	`X`	`SPA`	`@0 Canal con ruido @5 08`
C03	`09`	`X`	`FRE`	`@0 Correction erreur @5 09`
C03	`09`	`X`	`ENG`	`@0 Error correction @5 09`
C03	`09`	`X`	`SPA`	`@0 Corrección error @5 09`
C03	`10`	`X`	`FRE`	`@0 Décodage Viterbi @5 10`
C03	`10`	`X`	`ENG`	`@0 Viterbi decoding @5 10`
C03	`10`	`X`	`SPA`	`@0 Desciframiento Viterbi @5 10`
C03	`11`	`X`	`FRE`	`@0 Reconnaissance forme @5 31`
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C03	`12`	`X`	`SPA`	`@0 Tratamiento palabra @5 32`
C03	`13`	`X`	`FRE`	`@0 Estimation paramètre @5 33`
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N44	`01`			`@1 OTO`
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A30	`01`	`1`	`ENG`	`@1 ICFHR 2008 Conference @3 Montreal CAN @4 2008-08-18`

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Le document en format XML

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<front><div type="abstract" xml:lang="en">We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a "phrase-based" system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.</div>
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<fC01 i1="01" l="ENG"><s0>We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a "phrase-based" system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.</s0>
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<s5>31</s5>
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<s5>32</s5>
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<s5>32</s5>
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<s5>32</s5>
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<s5>33</s5>
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   |texte=   Phrase-based correction model for improving handwriting recognition accuracies
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Phrase-based correction model for improving handwriting recognition accuracies

Phrase-based correction model for improving handwriting recognition accuracies

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