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Minimum error rate training for PHMM-based text recognition.

C Yen, S S Kuo, C H Lee

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 13, 2008
    PubMed
    Summary
    This summary is machine-generated.

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    Discriminative training significantly reduces errors in pseudo 2-D hidden Markov model (PHMM) text recognition. This method improves performance beyond conventional maximum likelihood training for better accuracy.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Investigating discriminative training for pseudo two-dimensional hidden Markov models (PHMMs).
    • Focusing on enhancing text recognition system performance.
    • Addressing limitations of conventional maximum likelihood (ML) training.

    Discussion:

    • Discriminative training adjusts model parameters to directly minimize classification error rates.
    • This approach contrasts with ML methods that maximize data likelihood.
    • Evaluating the impact of discriminative training on PHMMs for text recognition.

    Key Insights:

    • Significant reduction in recognition error rates observed.
    • Discriminative training outperforms conventional ML approaches, even on well-trained models.

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  • Demonstrates the effectiveness of error-driven training for PHMM-based systems.

    • Outlook:

    • Potential for further improvements in text recognition accuracy.
    • Exploring applications of discriminative training in other pattern recognition tasks.
    • Advancing PHMMs for more robust and efficient text recognition.