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Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
Comparing Natural and Synthetic Training Data for Off-Line Cursive Handwriting Recognition
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
Tamás Varga, Universität Bern
Horst Bunke, Universität Bern
In this paper, a perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text, produced by human writers, is presented. The goal of synthetic textline generation is to improve the performance of an off-line cursive handwriting recognition system by providing it with additional, synthetic training data. In earlier papers, it has been shown that it is possible to improve the recognition performance by using such synthetically expanded training sets. In this paper, we investigate the suitability of synthetically generated handwriting when enlarging the training set of a handwriting recognition system in a more rigorous way. In particular, the improvements achieved with synthetic training data are compared to those achieved by expanding the training set using natural, i.e. human written, textlines.
Index Terms:
off-line cursive handwriting recognition, training set expansion, synthetic training data, perturbation model, hidden Markov model (HMM).
Citation:
Tamás Varga, Horst Bunke, "Comparing Natural and Synthetic Training Data for Off-Line Cursive Handwriting Recognition," iwfhr, pp.221-225, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
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