Off-Line Writer Verification: A Comparison of a Hidden Markov Model (HMM) and a Gaussian Mixture Model (GMM) Based System

Abstract : In this paper, we introduce and compare two off-line, text independent writer verification systems. At the core of the first system are Hidden Markov Model (HMM) based recognizers. The second system uses Gaussian Mixture Models (GMMs) to model a person's handwriting. Both systems are evaluated on two test sets consisting of unskillfully forged and skillfully forged text lines, respectively. In this comparison, different confidence measures are considered, based on the raw log-likelihood score, the cohort model approach, and the world model approach.
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Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
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Soumis le : vendredi 20 octobre 2006 - 16:17:40
Dernière modification le : lundi 20 juin 2016 - 14:10:32
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  • HAL Id : inria-00108410, version 1

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Andreas Schlapbach, Horst Bunke. Off-Line Writer Verification: A Comparison of a Hidden Markov Model (HMM) and a Gaussian Mixture Model (GMM) Based System. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00108410〉

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