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|a 10.1109/83.704304
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|a eng
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|a Ganis, M D
|e verfasserin
|4 aut
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|a Neural network-based systems for handprint OCR applications
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|c 1998
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|a Date Completed 16.12.2009
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|a Date Revised 15.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized
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|a Journal Article
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|a Wilson, C L
|e verfasserin
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|a Blue, J L
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 7(1998), 8 vom: 30., Seite 1097-112
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