Abstract
This paper evaluates the application of minimum classification error (MCE) training to online-handwritten text recognition based on hidden Markov models. We describe an allograph-based, character level MCE training aimed at minimizing the character error rate while enabling flexibility in writing style. Experiments on a writer-independent discrete character recognition task, covering all alpha-numerical characters and keyboard symbols, show that MCE achieves more than 30% character error rate reduction compared to the baseline maximum likelihood-based system.
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