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Minimum classification error training for online handwritten word recognition

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1 Author(s)
Biem, A. ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

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.

Published in:

Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on

Date of Conference:

2002