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Publication
IWFHR 2002
Conference paper
Minimum classification error training for online handwritten word recognition
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. © 2002 IEEE.