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Neural network-based systems for handprint OCR applications.

M D Ganis1, C L Wilson, J L Blue

  • 1Nat. Inst. of Stand. and Technol., Gaithersburg, MD 20899, USA. mgarris@nist.gov

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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Neural networks, specifically enhanced multilayer perceptrons, offer improved optical character recognition (OCR) for handprint data. NIST developed a system using novel neuron activation functions and regularization techniques for better performance.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neural network (NN) approaches have significantly advanced optical character recognition (OCR) performance.
  • Handprint recognition remains a challenging area within OCR applications.

Purpose of the Study:

  • To present an enhanced neural network classification scheme for form-based handprint OCR.
  • To describe an end-to-end OCR system developed by NIST.

Main Methods:

  • Utilized an enhanced multilayer perceptron (MLP) with novel neuron activation functions.
  • Implemented successive regularization and Boltzmann pruning to optimize the MLP's weight space.
  • Developed an end-to-end system for form-based handprint recognition.

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Main Results:

  • The enhanced MLP design addresses singular Jacobians, constraining weight space volume and dimension.
  • Performance characterization studies were conducted on NN systems, including the NIST system.
  • Evaluations were performed at the first OCR systems conference.

Conclusions:

  • The presented NN classification scheme and NIST system offer advancements in handprint OCR.
  • The enhancements to the MLP contribute to improved accuracy and robustness in character recognition.
  • Further performance characterization validates the effectiveness of these neural network approaches.