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A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters
November 2009 (vol. 31 no. 11)
pp. 2060-2072
ASCII Text
x 
 
Moussa Djioua, Réjean Plamondon, "A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2060-2072, November, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2008.264,
author = {Moussa Djioua and Réjean Plamondon},
title = {A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
number = {11},
issn = {0162-8828},
year = {2009},
pages = {2060-2072},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.264},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters
IS - 11
SN - 0162-8828
SP2060
EP2072
EPD - 2060-2072
A1 - Moussa Djioua,
A1 - Réjean Plamondon,
PY - 2009
KW - Pattern recognition
KW - kinematic theory
KW - rapid movement
KW - Delta-Lognormal model
KW - lognormal function
KW - parameter extraction
KW - motor control
KW - nonlinear regression
KW - optimization
KW - curve-fitting.
VL - 31
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Moussa Djioua, École Polytechnique de Montréal, Montréal
Réjean Plamondon, École Polytechnique de Montréal, Montréal
In this paper, we present a new analytical method for estimating the parameters of Delta-Lognormal functions and characterizing handwriting strokes. According to the Kinematic Theory of rapid human movements, these parameters contain information on both the motor commands and the timing properties of a neuromuscular system. The new algorithm, called XZERO, exploits relationships between the zero crossings of the first and second time derivatives of a lognormal function and its four basic parameters. The methodology is described and then evaluated under various testing conditions. The new tool allows a greater variety of stroke patterns to be processed automatically. Furthermore, for the first time, the extraction accuracy is quantified empirically, taking advantage of the exponential relationships that link the dispersion of the extraction errors with its signal-to-noise ratio. A new extraction system which combines this algorithm with two other previously published methods is also described and evaluated. This system provides researchers involved in various domains of pattern analysis and artificial intelligence with new tools for the basic study of single strokes as primitives for understanding rapid human movements.

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Index Terms:
Pattern recognition, kinematic theory, rapid movement, Delta-Lognormal model, lognormal function, parameter extraction, motor control, nonlinear regression, optimization, curve-fitting.
Citation:
Moussa Djioua, Réjean Plamondon, "A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2060-2072, Nov. 2009, doi:10.1109/TPAMI.2008.264
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