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Communication Dans Un Congrès Année : 2010

Font adaptation of an HMM-based OCR system

Résumé

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.
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Dates et versions

hal-01026429 , version 1 (21-07-2014)

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  • HAL Id : hal-01026429 , version 1

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Kamel Ait-Mohand, Laurent Heutte, Thierry Paquet, Nicolas Ragot. Font adaptation of an HMM-based OCR system. 17th Document Recognition and Retrieval Conference, part of the IS&T/SPIE International Symposium on Electronic Imaging,, Jan 2010, San Jose, United States. ⟨hal-01026429⟩
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