Bibtex entry:

@inproceedings{icpr92:Kov,
   key = {icpr92:Kov},
   keywords = {ocr, classifiers, algorithms, nearest neighbors},
   author = {Zs. M. Kovacs-V. and R. Guerrieri and G. Baccarani},
   title = {A Novel Metric for Nearest-Neighbor Classification of
            Hand-Written Digits},
   booktitle = {Proc. of 11th ICPR 1992 The Hague - The Netherlands},
   volume = {2},
   pages = {96--100},
   month = {Aug},
   year = {1992},
   abstract={
Classifiers based on the k-Nearest Neighbors (k-NN) approach have
recently received an increasing attention because of their simple
implementation and absence of training. In this technique, the
similarity measure used to compute the distance between the stored
patterns and the test element is the most crucial part of the method.
The paper addresses this issue within the context of recognition of
hand-written digits.  A novel similarity measure is proposed and used
to associate a number to each pair of samples in a suitable
N-dimensional space in order to define the distance between two
handwritten characters. The proposed similarity measure has been
parameterized and the best values of these parameters have been
evaluated using suitable statistical approaches. Finally, some results
obtained from the classification of digits extracted from a ZIP code
database are provided.
},
}

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