University of Bologna

Dipartimento di Elettronica, Informatica e Sistemistica

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Searched for "pr:Kov95a"
Clicking on the number of each publication you can retrieve the bibtex entry. If available, the abstract can be accessed clicking on the title of the work.

Bibtex entry:

   keywords = {ocr, pattern recognition, nearest neighbors, algorithms},
   author = {Zs. M. Kovacs and R. Guerrieri},
   title="Massively-Parallel Hand-Written Character Recognition Based
          on the Distance Transform",
   journal= {Pattern Recognition},
   volume = {28},
   number = {3},
   pages = {293--301},
   month = {Mar},
   year = {1995},
A new 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) into a gray scale
picture. 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, Connection Machine
CM-2. Classification results of digits extracted from the U.S. Post
Office ZIP code database and the upper-case letters of the NIST Test
Data 1 are provided. The system has an accuracy of 96.73% on the
digits and 94.51% on the upper-case letters when no rejection is
allowed and an accuracy of 98.96% on the digits and 98.72% on the
upper-case letters at 1% error rate.

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