Bibtex entry:

@inproceedings{cm93:Kov,
   key = {cm93:Kov},
   keywords = {ocr, pattern recognition, classifiers, algorithms,
               nearest neighbors},
   author = {Zs. M. Kovacs-V. and R. Guerrieri},
   title = {Nearest-Neighbors Classification of Hand-Written Digits
            Using the CM-2},
   booktitle = {Atti della PARMA-CM Users Meeting - Parma, Italy},
   pages = {196--201},
   month = {Jul},
   year = {1993},
   abstract={
A statistical classifier for hand-written character recognition is
presented. After a standard preprocessing phase for image binarization
and normalization, a distance transform is applied to the normalized
image, converting a black and white (B/W) picture into a gray scale
one. The latter is used as feature space for a k-Nearest-Neighbor
classifier, based on a dissimilarity measure which generalizes the use
of the distance transform itself. The classifier has been implemented
on a massively parallel processor, the Connection Machine
CM-2. Results obtained from the classification of digits extracted
from the U.S. Post Office ZIP code database are provided. The system
has an accuracy of 96.73% on the digits when no rejection is allowed
and it has an accuracy of 98.96% at 1% error rate.
},
}

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