By Topic

Large-set handwritten character recognition with multiple stochastic models

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)
Hee-Seon Park ; Dept. of Comput. Sci., Chungbuk Nat. Univ., South Korea ; Seong-Whan Lee

An efficient recognition scheme for large-set handwritten characters is proposed in the framework of multiple stochastic models, in this case, first order hidden Markov models which can model stochastically the input pattern with numerous variations. In this scheme, after extracting four kinds of regional projection contours for an input pattern by using the regional projection contour transformation, four kinds of HMMs are constructed during the training phase based on the direction components of these contours. In the recognition phase, the four kinds of HMMs constructed in the training phase are combined to output the final recognition result for an input pattern

Published in:

Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on

Date of Conference:

20-22 Oct 1993