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Minimum error rate training for PHMM-based text recognition
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DOI：10.1109/83.777092.png)
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Abstract
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In this work, discriminative training is studied to improve the performance of our pseudo two-dimensional (2-D) hidden Markov model (PHMM) based text recognition system. The aim of this discriminative training is to adjust model parameters to directly minimize the classification error rate. Experimental results have shown great reduction in recognition error rate even for PHMM's already well-trained using conventional maximum likelihood (ML) approaches.
Keywords:
discriminative training
document image processing
hidden Markov models
image recognition
learning system
optical character recognition
pattern recognition
stochastic models
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