By Topic

Fuzzy algorithm for contextual character recognition

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

2 Author(s)
Tembe, W. ; Dept. of ECECS, Cincinnati Univ., OH, USA ; Ralescu, A.

Measuring the likeness between data in different ways is an important part of pattern recognition and, over the years, many such measures have been developed. This paper proposes an asymmetric measure of likeness based on the concept of context dependent divergence. This is used to construct a numerical descriptor for images and, in conjunction with fuzzy sets, to develop a supervised learning algorithm. When applied to the problem of handwritten digit recognition, the algorithm produces promising and highly accurate results.

Published in:

Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on  (Volume:3 )

Date of Conference:

25-29 July 2004