Title :
The effect of large training set sizes on online Japanese Kanji and English cursive recognizers
Author :
Rowley, Henry A. ; Goyal, Manish ; Bennett, John
Author_Institution :
Microsoft Corp., Redmond, WA, USA
Abstract :
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.
Keywords :
handwriting recognition; learning (artificial intelligence); Japanese Kanji recognizer; constrained training set; cursive English recognizer; handwriting recognition; neural-network; recognizers; training data; Character recognition; Delay effects; Frequency; Handwriting recognition; Ink; Neural networks; Prototypes; Sparse matrices; Training data; Writing;
Conference_Titel :
Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on
Print_ISBN :
0-7695-1692-0
DOI :
10.1109/IWFHR.2002.1030881