DocumentCode
2219416
Title
Minimum classification error training for online handwritten word recognition
Author
Biem, Alain
Author_Institution
IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
fYear
2002
fDate
2002
Firstpage
61
Lastpage
66
Abstract
We describe an application of the minimum classification error (MCE) training criterion to online unconstrained-style word recognition. The described system uses allograph-HMMs to handle writer variability. The result, on vocabularies of 5k to 10k, shows that MCE training achieves around 17% word error rate reduction when compared to the baseline maximum likelihood system.
Keywords
Bayes methods; decision theory; handwritten character recognition; hidden Markov models; optimisation; parameter estimation; probability; allograph-HMMs; baseline maximum likelihood system; hidden Markov modeling; minimum classification error training; online handwritten word recognition; online unconstrained-style word recognition; writer variability; Error analysis; Handheld computers; Handwriting recognition; Hidden Markov models; Personal digital assistants; Shape; Signal processing; Speech recognition; Vocabulary; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on
Print_ISBN
0-7695-1692-0
Type
conf
DOI
10.1109/IWFHR.2002.1030885
Filename
1030885
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