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.
Minimum classification error training for online handwritten word recognition
01.01.2002
300616 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Minimum Classification Error Training for Online Handwritten Word Recognition
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