OcrV1, Main, Exploration, bibRecord, 000781

Font adaptation of an HMM-based OCR system

Identifieur interne : 000781 ( Main/Exploration ); précédent : 000780; suivant : 000782

Font adaptation of an HMM-based OCR system

Auteurs : Kamel Ait-Mohand [France] ; Laurent Heutte [France] ; Thierry Paquet [France] ; Nicolas Ragot [France]

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.

RBID : Pascal:10-0429723

Descripteurs français

Pascal (Inist)
- Algorithme, Reconnaissance forme, Recherche documentaire, Modèle Markov variable cachée, Reconnaissance optique caractère, Evaluation performance, Précision, Approche probabiliste, 0130C, 4230S.
Wicri :
- topic : Recherche documentaire.

English descriptors

KwdEn :
- Accuracy, Algorithms, Document retrieval, Hidden Markov models, Optical character recognition, Pattern recognition, Performance evaluation, Probabilistic approach.

Abstract

We create a polyfont OCR recognizer using HMM (Hidden Markov models) models of character trained on a dataset of various fonts. We compare this system to monofont recognizers showing its decrease of performance when it is used to recognize unseen fonts. In order to fill this gap of performance, we adapt the parameters of the models of the polyfont recognizer to a new dataset of unseen fonts using four different adaptation algorithms. The results of our experiments show that the adapted system is far more accurate than the initial system although it does not reach the accuracy of a monofont recognizer.

Affiliations:

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

to stream PascalFrancis, to step Corpus: 000160
to stream PascalFrancis, to step Curation: 000617
to stream PascalFrancis, to step Checkpoint: 000155
to stream Main, to step Merge: 000786
to stream Main, to step Curation: 000781

Le document en format XML

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<front><div type="abstract" xml:lang="en">We create a polyfont OCR recognizer using HMM (Hidden Markov models) models of character trained on a dataset of various fonts. We compare this system to monofont recognizers showing its decrease of performance when it is used to recognize unseen fonts. In order to fill this gap of performance, we adapt the parameters of the models of the polyfont recognizer to a new dataset of unseen fonts using four different adaptation algorithms. The results of our experiments show that the adapted system is far more accurate than the initial system although it does not reach the accuracy of a monofont recognizer.</div>
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Font adaptation of an HMM-based OCR system

Font adaptation of an HMM-based OCR system

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