Cart (Loading....) | Create Account
Close category search window

A Probabilistic Framework for Soft Target Learning in Online Cursive Handwriting 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

4 Author(s)
Xiaoyuan Zhu ; Motorola Shanghai Lab., Shanghai, China ; Yong Ge ; Fengjun Guo ; Lixin Zhen

To develop effective learning algorithms for online cursive word recognition is still a challenge research issue. In this paper, we propose a probabilistic framework to model the inherent ambiguity of cursive handwriting by using soft target vector of each character class. In the proposed algorithm, the values of soft targets are estimated by introducing a lower bound on the log likelihood and optimizing this lower bound via an EM like algorithm. In the experiments on 207 K collected cursive words written by 1060 subjects, the proposed algorithm clearly outperforms baseline method with word error reduction up to 11.6%. Furthermore, the estimated soft target values are useful for measuring the separability between output classes.

Published in:

Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on

Date of Conference:

26-29 July 2009

Need Help?

IEEE Advancing Technology for Humanity 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 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.