By Topic

Offline cursive word recognition using continuous density hidden Markov models trained with PCA or ICA features

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)
Vinciarelli, A. ; IDIAP-Inst. Dalle Molle d''Intelligence Artificielle Perceptive, Martigny, Switzerland ; Bengio, S.

This work presents an offline cursive word recognition system dealing with single writer samples. The system is based on a continuous density hidden Markov model trained using either the raw data, or data transformed using principal component analysis or independent component analysis. Both techniques significantly improved the recognition rate of the system. Preprocessing, normalization and feature extraction are described as well as the training technique adopted. Several experiments were performed using a publicly available database. The accuracy obtained is the highest presented in the literature over the same data.

Published in:

Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:3 )

Date of Conference:

2002