Minimum Classification Error Training for Online Handwritten Word Recognition

Authors:
Alain Biem
Affiliations:
-
Venue:
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Year:
2002

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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 trainingachieves around 17% word error rate reduction when compared to the baseline Maximum Likelihood system.