On finding optimal parameters of an oscillatory model of handwriting

Gaëtan André; Frédéric Messine

RAIRO - Operations Research - Recherche Opérationnelle (2014)

  • Volume: 48, Issue: 4, page 509-520
  • ISSN: 0399-0559

Abstract

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In this paper, we show how optimization methods can be used efficiently to determine the parameters of an oscillatory model of handwriting. Because these methods have to be used in real-time applications, this involves that the optimization problems must be rapidely solved. Hence, we developed an original heuristic algorithm, named FHA. This code was validated by comparing it (accuracy/CPU-times) with a multistart method based on Trust Region Reflective algorithm.

How to cite

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André, Gaëtan, and Messine, Frédéric. "On finding optimal parameters of an oscillatory model of handwriting." RAIRO - Operations Research - Recherche Opérationnelle 48.4 (2014): 509-520. <http://eudml.org/doc/275020>.

@article{André2014,
abstract = {In this paper, we show how optimization methods can be used efficiently to determine the parameters of an oscillatory model of handwriting. Because these methods have to be used in real-time applications, this involves that the optimization problems must be rapidely solved. Hence, we developed an original heuristic algorithm, named FHA. This code was validated by comparing it (accuracy/CPU-times) with a multistart method based on Trust Region Reflective algorithm.},
author = {André, Gaëtan, Messine, Frédéric},
journal = {RAIRO - Operations Research - Recherche Opérationnelle},
keywords = {handwriting model; nonlinear programming; heuritic method; multistart method},
language = {eng},
number = {4},
pages = {509-520},
publisher = {EDP-Sciences},
title = {On finding optimal parameters of an oscillatory model of handwriting},
url = {http://eudml.org/doc/275020},
volume = {48},
year = {2014},
}

TY - JOUR
AU - André, Gaëtan
AU - Messine, Frédéric
TI - On finding optimal parameters of an oscillatory model of handwriting
JO - RAIRO - Operations Research - Recherche Opérationnelle
PY - 2014
PB - EDP-Sciences
VL - 48
IS - 4
SP - 509
EP - 520
AB - In this paper, we show how optimization methods can be used efficiently to determine the parameters of an oscillatory model of handwriting. Because these methods have to be used in real-time applications, this involves that the optimization problems must be rapidely solved. Hence, we developed an original heuristic algorithm, named FHA. This code was validated by comparing it (accuracy/CPU-times) with a multistart method based on Trust Region Reflective algorithm.
LA - eng
KW - handwriting model; nonlinear programming; heuritic method; multistart method
UR - http://eudml.org/doc/275020
ER -

References

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  1. [1] G. André, www.irit.fr/∼Gaetan.Andre/publications.php. 
  2. [2] T.F. Coleman and Y. Li, An interior trust region approach for nonlinear minimization subject to bounds. SIAM J. Opt.6 (1993) 418–445. Zbl0855.65063MR1387333
  3. [3] T.F. Coleman and Y. Li, On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Math. Program.67 (1994) 189–224. Zbl0842.90106MR1305566
  4. [4] E. Gilet, Modélisation bayésienne d’une boucle de perception action : Application al’écriture (Bayesian Modelisation of a sensori-motor loop: application to reading and handwriting). Thesis, Joseph, Fourier University, Grenoble, France (2009). 
  5. [5] J.M. Hollerbach, An oscillatory theory of handwriting. Biol. Cybern.156 (1981) 139–156. 
  6. [6] M. Longcamp et al., The imprint of action: motor cortex involvement in visual perception of handwritten letters. NeuroImage23 (2006) 681–688. 
  7. [7] R. Plamondon et al., Modelling velocity profiles of rapid movements: a comparative study. Biol. Cybern.69 (1993) 119–128. 
  8. [8] R. Plamondon, On-Line and Off-Line, Handwriting Recognition: A Comprehensive Survey. IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000) 63–84. 
  9. [9] T. Plötz and G. Fink, Markov models for offline handwriting recognition: a survey. Int. J. Doc. Anal. Recogn. (IJDAR) 12 (2009) 169–298. 
  10. [10] P. Viviani and T. Flash, Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. J. Exp. Psychol. Hum. Percept. Perform.21 (1995) 32–53. 

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