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Proceedings Paper

Font adaptation of an HMM-based OCR system
Author(s): Kamel Ait-Mohand; Laurent Heutte; Thierry Paquet; Nicolas Ragot
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Paper 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.

Paper Details

Date Published: 18 January 2010
PDF: 8 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340J (18 January 2010); doi: 10.1117/12.840321
Show Author Affiliations
Kamel Ait-Mohand, Univ. de Rouen (France)
Laurent Heutte, Univ. de Rouen (France)
Thierry Paquet, Univ. de Rouen (France)
Nicolas Ragot, Univ. François Rabelais Tours (France)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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