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Neural-network classifiers for recognizing totally unconstrained handwritten numerals.

S B Cho1

  • 1Dept. of Comput. Sci., Yonsei Univ., Seoul.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary
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This study introduces three advanced artificial neural network classifiers for complex pattern recognition, achieving high accuracy in handwritten numeral recognition. These novel methods demonstrate superior performance over existing techniques on a challenging benchmark dataset.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial neural networks (ANNs) are effective for pattern classification.
  • Simple ANNs struggle with complex tasks like handwritten numeral recognition.
  • Advanced neural network architectures are needed for difficult pattern recognition challenges.

Purpose of the Study:

  • To present three sophisticated neural network classifiers for complex pattern recognition.
  • To evaluate the performance of these novel classifiers on a challenging dataset.
  • To demonstrate the superiority of the proposed methods over existing approaches.

Main Methods:

  • Multiple Multilayer Perceptron (MLP) classifier
  • Hidden Markov Model (HMM)/MLP hybrid classifier

Related Experiment Videos

  • Structure-adaptive Self-Organizing Map (SOM) classifier

    • Main Results:

    • The proposed MLP, HMM/MLP, and SOM classifiers achieved recognition rates of 97.35%, 96.55%, and 96.05%, respectively.
    • These results surpass previously reported methods on the same unconstrained handwritten numeral database.
    • Experimental validation was conducted using the Concordia University unconstrained handwritten numeral database.

    Conclusions:

    • The developed neural network classifiers offer significant improvements for complex pattern recognition tasks.
    • These sophisticated models effectively address the limitations of straightforward neural network approaches.
    • The findings highlight the potential of advanced neural network designs for handwritten numeral recognition.