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Offline Cursive Word Recognition using Continuous Density Hidden Markov Models Trained with PCA or ICA Features
Quebec City, QC, Canada August 11-August 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.104780016th International Conference on Patt ...
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A. Vinciarelli, S. Bengio, "Offline Cursive Word Recognition using Continuous Density Hidden Markov Models Trained with PCA or ICA Features," Pattern Recognition, International Conference on, vol. 3, pp. 30081, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 3, 2002.
 
 
BibTex
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@article{ 10.1109/ICPR.2002.1047800,
author = {A. Vinciarelli and S. Bengio},
title = {Offline Cursive Word Recognition using Continuous Density Hidden Markov Models Trained with PCA or ICA Features},
journal ={Pattern Recognition, International Conference on},
volume = {3},
year = {2002},
issn = {1051-4651},
pages = {30081},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2002.1047800},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Offline Cursive Word Recognition using Continuous Density Hidden Markov Models Trained with PCA or ICA Features
SN - 1051-4651
SP
EP
A1 - A. Vinciarelli,
A1 - S. Bengio,
PY - 2002
KW - null
VL - 3
JA - Pattern Recognition, International Conference on
ER -
 
A. Vinciarelli, Institut Dalle Molle d?Intelligence Artificielle Perceptive
S. Bengio, Institut Dalle Molle d?Intelligence Artificielle Perceptive

This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is based on a continuous density Hiddden 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.

Citation:
A. Vinciarelli, S. Bengio, "Offline Cursive Word Recognition using Continuous Density Hidden Markov Models Trained with PCA or ICA Features," icpr, vol. 3, pp.30081, 16th International Conference on Pattern Recognition (ICPR'02) - Volume 3, 2002
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