Referenced Items are not available for this document.
This paper addresses the problem of recognizing on-line sampled handwritten symbols. Within the proposed symbol recognition system based on hidden Markov models different kinds of feature extraction algorithms are used analysing on-line features as well as off-line features and combining the classification results. By conducting writer-dependent recognition experiments, it is demonstrated that the recognition rates as well as the reliability of the results is improved by using the proposed recognition system. Furthermore, by applying handwriting data not representing symbols out of the given alphabet, an increase of their rejection rate is obtained
Published in:
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
(Volume:6
)
Date of Conference: 7-10 May 1996
- Page(s):
- 3438 - 3441 vol. 6
- Meeting Date :
- 07 May 1996-10 May 1996
- ISSN :
- 1520-6149
- Print ISBN:
- 0-7803-3192-3
- INSPEC Accession Number:
- 5501524
- Conference Location :
- Atlanta, GA
- Digital Object Identifier :
- 10.1109/ICASSP.1996.550767
- Product Type:
- Conference Publications
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
