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Off-line handwritten textline recognition using a mixture of natural and synthetic training data | IEEE Conference Publication | IEEE Xplore

Off-line handwritten textline recognition using a mixture of natural and synthetic training data


Abstract:

In this paper the problem of off-line handwritten cursive text recognition is considered. A method for expanding the set of available training textlines by applying rando...Show More

Abstract:

In this paper the problem of off-line handwritten cursive text recognition is considered. A method for expanding the set of available training textlines by applying random perturbations is presented. The goal is to improve the recognition performance of an off-line handwritten textline recognizer by providing it with additional synthetic training data. Three important issues - quality, variability, and capacity - related to this method are discussed, and a basic strategy to make use of the possibility of expanding the training set by synthetic textlines is proposed. It is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by many different writers.
Date of Conference: 26-26 August 2004
Date Added to IEEE Xplore: 20 September 2004
Print ISBN:0-7695-2128-2
Print ISSN: 1051-4651
Conference Location: Cambridge, UK

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