By Topic

Hidden control neural network and HMM hybrid approach for on-line 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)
Ma Lin ; Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., China ; Li Haifeng ; Han Jiqing ; Gallinari, P.

The paper focuses on a hidden control neural network (HCNN) based ANN/HMM hybrid approach which handles simultaneously both the global pattern class variation and the local signal primitive variation. HMM is used at the pattern class level to organise different primitives in various orders. One HCNN is applied to model signal primitives in each HMM state as the emission probability estimator. The control signal of HCNN copes with the primitive variation absorption task. The proposed method was applied to the on-line cursive handwriting recognition problem and compared with our previous similar systems on the UNIPEN handwriting database.

Published in:

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on  (Volume:1 )

Date of Conference:

14-17 Dec. 2003