By Topic

Data driven design of an ANN/HMM system for on-line unconstrained handwritten 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
$33 $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

3 Author(s)
Haifeng Li ; Comput. Sci. Lab., Univ. Paris 6, France ; T. Artieres ; P. Gallinari

This paper is dedicated to a data driven design method for a hybrid ANN/HMM based handwriting recognition system. On one hand, a data driven designed neural modelling of handwriting primitives is proposed. ANNs are firstly used as state models in a HMM primitive divider that associates each signal frame with an ANN by minimizing the accumulated prediction error. Then, the neural modelling is realized by training each network on its own frame set. Organizing these two steps in an EM algorithm, precise primitive models are obtained. On the other hand, a data driven systematic method is proposed for the HMM topology inference task. All possible prototypes of a pattern class are firstly merged into several clusters by a tabu search aided clustering algorithm. Then a multiple parallel-path HMM is constructed for the pattern class. Experiments prove an 8% recognition improvement with a saving of 50% of system resources, compared to an intuitively designed referential ANN/HMM system.

Published in:

Multimodal Interfaces, 2002. Proceedings. Fourth IEEE International Conference on

Date of Conference: