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This paper describes an application of the Minimum Classification Error (MCE) criterion to the problem of recognizing online unconstrained-style characters and words. We describe an HMM-based, character and word-level MCE training aimed at minimizing the character or word error rate while enabling flexibility in writing style through the use of multiple allographs per character. Experiments on a writer-independent character recognition task covering alpha-numerical characters and keyboard symbols show that the MCE criterion achieves more than 30 percent character error rate reduction compared to the baseline Maximum Likelihood-based system. Word recognition results, on vocabularies of 5k to 10k, show that MCE training achieves around 17 percent word error rate reduction when compared to the baseline Maximum Likelihood system.
Minimum classification error, hidden Markov model, handwriting recognition, maximum likelihood, discriminative training, dynamic programming, finite state machine.
Alain Biem, "Minimum Classification Error Training for Online Handwriting Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1041-1051, July 2006, doi:10.1109/TPAMI.2006.146
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