By Topic

Comparisons of a neural network and a nearest-neighbor classifier via the numeric handprint recognition problem

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Weideman, W.E. ; Voice Control Syst., Dallas, TX, USA ; Manry, M.T. ; Yau, H.-C. ; Wei Gong

A comparison is made of two techniques for recognizing numeric handprint characters using a variety of features including 2D fast Fourier transform coefficients, geometrical moments, and topological features. A backpropagation network and a nearest neighbor classifier are evaluated in terms of recognition performance and computational requirements. The results indicate that for complex problems, the neural network performs comparably to the nearest-neighbor classifier while being significantly more cost effective

Published in:

Neural Networks, IEEE Transactions on  (Volume:6 ,  Issue: 6 )