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Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
The Effect of Large Training Set Sizes on Online Japanese Kanji and English Cursive Recognizers
Ontario, Canada
August 06-August 08
ISBN: 0-7695-1692-0
Henry A. Rowley, Microsoft Corporation
Manish Goyal, Microsoft Corporation
John Bennett, Microsoft Corporation
Much research in handwriting recognition has focused on how to improve recognizers with constrained training set sizes. This paper presents the results of training a nearest-neighbor based online Japanese Kanji recognizer and a neural-network based online cursive English recognizer on a wide range of training set sizes, including sizes not generally available. The experiments demonstrate that increasing the amount of training data improves the accuracy, even when the recognizer?s representation power is limited.
Citation:
Henry A. Rowley, Manish Goyal, John Bennett, "The Effect of Large Training Set Sizes on Online Japanese Kanji and English Cursive Recognizers," iwfhr, pp.36, Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02), 2002
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