AustralieFrV1, Main, Exploration, bibRecord, 007426

Unsupervised writer adaptation of whole-word HMMs with application to word-spotting

Identifieur interne : 007426 ( Main/Exploration ); précédent : 007425; suivant : 007427

Unsupervised writer adaptation of whole-word HMMs with application to word-spotting

Auteurs : José A. Rodriguez-Serrano [France, Espagne] ; Florent Perronnin [France] ; Gemma Sanchez [Espagne] ; Josep Llados [Espagne]

Source :

Pattern recognition letters [ 0167-8655 ] ; 2010.

RBID : Pascal:10-0223708

Descripteurs français

Pascal (Inist)
- Classification non supervisée, Modèle Markov variable cachée, Caractère manuscrit, Processus semi markovien, Table codage, Méthode statistique, Mot clé, Evaluation performance, Reconnaissance forme, Reconnaissance écriture, Analyse documentaire, Classification signal, Approche probabiliste.
Wicri :
- topic : Méthode statistique.

English descriptors

KwdEn :
- Codebook, Document analysis, Handwriting recognition, Hidden Markov models, Keyword, Manuscript character, Pattern recognition, Performance evaluation, Probabilistic approach, Semimarkovian process, Signal classification, Statistical method, Unsupervised classification.

Abstract

In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters. Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition.

Affiliations:

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

to stream PascalFrancis, to step Corpus: 002709
to stream PascalFrancis, to step Curation: 003877
to stream PascalFrancis, to step Checkpoint: 001E98
to stream Main, to step Merge: 007966
to stream Main, to step Curation: 007426

Le document en format XML

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<front><div type="abstract" xml:lang="en">In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters. Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition.</div>
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Unsupervised writer adaptation of whole-word HMMs with application to word-spotting

Unsupervised writer adaptation of whole-word HMMs with application to word-spotting

Source :

Descripteurs français

English descriptors

Abstract

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

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri