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Genetic Algorithms for Perceptual Codes Extraction

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DOI: 10.4236/jilsa.2012.44026    2,733 Downloads   4,572 Views   Citations


In this work a new technique for global perceptual codes (GPCs) extraction using genetic algorithms (GA) is presented. GAs are employed to extract the GPCs in order to reduce the original number of features and to provide meaningful representations of the original data. In this technique the GPCs are build from a certain combination of elementary perceptual codes (EPCs) which are provided by the Beta-elliptic model for the generation of complex handwriting movements. Indeed, in this model each script is modelled by a set of elliptic arcs. We associate to each arc an EPC. In the proposed technique we defined four types of EPCs. The GPCs can be formed by many possible combinations of EPCs depending on their number and types. So that, the problem of choosing the right combination for each GPC can be regarded as a global optimization problem which is treated in this work using the GAs. Several simulation examples are presented to evaluate the interest and the efficiency of the proposed technique.

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M. Ltaief, S. Njah, H. Bezine and A. Alimi, "Genetic Algorithms for Perceptual Codes Extraction," Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 4, 2012, pp. 255-265. doi: 10.4236/jilsa.2012.44026.


[1] E. Anquetil and G. Lorette, “Perceptual Model of Handwriting Drawing Application to the Handwriting Segmentation Problem,” International Conference on Document Analysis and Recognition, Ulm, 18-20 August 1997, pp. 112-117.
[2] P. Viviani and N. Stucchi, “Biological Movements Look Uniform: Evidence of Motor-Perceptual Interactions,” Journal of Experimental Psychology: Human Perception and Performance, Vol. 18, No. 3, 1992, pp. 603-623. doi:10.1037/0096-1523.18.3.603
[3] D. Connell Scott, “On Line Handwritten Recognition Using Multiple Pattern Class Models,” Ph.D. Thesis, Université de Michigan state, East Lansing, 2000.
[4] A. L. Koerich, R. Sabourin and C. Y. Suen, “Large Vocabulary Off-Line Handwriting Recognition: A Survey,” Pattern Analytic Application, Vol. 6, No. 2, 2003, pp. 97-121.
[5] A. Pavlidis, “36 Years on the Pattern Recognition Front,” Pattern Recognition Letters, Vol. 24, No. 1-3, 2003, pp. 1-7. doi:10.1016/S0167-8655(02)00211-8
[6] R. Plamondon and S. N. Srihari, “On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 63-84.
[7] M. C?té, E. Lecolinet, M. Cheriet and C. Y. Suen, “Automatic Reading of Cursive Scripts Using a Reading Model and Perceptual Concepts, The PERCEPTO System,” International Journal on Document Analysis and Recognition, Vol. 1, No. 1, 1998, pp. 3-17. doi:10.1007/s100320050002
[8] S. S. Maddouri, “A Neural Perceptual Model Combining a Local and Global Vision for Arabic Handwritten Words Recognition,” Ph.D. Thesis, National School of Engineers of Tunis, Tunis, 2002.
[9] M. C?té, “The Use of Access Lexical Model and Perceptual Concepts for Image Recognition of Cursive Words,” Ph.D. Thesis, TELECOM ParisTech, Paris, 1997.
[10] J. R. Pinales, “Recognition of Offline Cursive Handwriting Using Perceptual and Neural Models,” Ph. D. Thesis, TELECOM ParisTech, Paris, 2001.
[11] D. Marr, “VISION: A Computational Investigation into Human Representation and Processing of Visual Information,” W. H. Freeman and Company, San Francisco, 1982, pp. 8-41.
[12] J. L. McClelland and D. E. Rumelhart, “Distributed Memory and the Representation of General and Specific Information,” Journal of Experimental Psychology: General, Vol. 114, No. 2, 1985, pp. 159-188. doi:10.1037/0096-3445.114.2.159
[13] Y. Wada, Y. Koike, E. Bateson and M. Kawato, “A Computational Model for Cursive Handwriting Based on the Minimization Principle,” Proceeding of the 7th Conference of the Advances in Neural Information Processing Systems 6, Denver, 29 November-2 December 1993, pp. 727-734.
[14] M. A. Alimi, “Beta Neuro-Fuzzy Systems,” TASK Quarterly Journal, Vol. 7, No. 1, 2003, pp. 23-41.
[15] M. A. Alimi, “A Neuro-Fuzzy Approach to Recognize Arabic Handwritten Characters,” International Conference on Neural Networks, Houston, 9-12 June 1997, pp. 1397-1400.
[16] H. Bezine, “Contribution to the Development of a Theory of Handwritten Generation,” Ph.D. Thesis, National School of Engineers of Sfax, Sfax, 2005.
[17] H. Bezine, M. A. Alimi and N. Sherkat, “Generation and Analysis of Handwriting Script with the Beta-Elliptic Model,” Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, Tokyo, 26-29 October 2004, pp. 515-520. doi:10.1109/IWFHR.2004.45
[18] H. Bezine, M. Kefi and M. A. Alimi, “On the Beta-Elliptic Model for the Control of the Human Arm Movement,” International Journal of Pattern Recognition and Artificial Intelligence, Vol. 21, No. 1, 2007, pp. 5-19. doi:10.1142/S0218001407005272
[19] F. L. Chung, T. C. Fu and R. W. P. Luk, “An Evolutionary Approach to Pattern-Based Time Series Segmentation,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 5, 2004, pp. 471-489. doi:10.1109/TEVC.2004.832863
[20] K. Deb, “Genetic Algorithm in Search and Optimization: The Technique and Applications,” Proceedings of International Workshop on Soft Computing and Intelligent Systems, Calcutta, 12-13 January 1998, pp. 58-87.
[21] S. Forrest, R. E. Smith, B. Jakornik and A. S. et Perelson, “Using Genetic Algorithms to Explore Pattern Recognition in the Immune System,” Evolutionary Computation, Vol. 1, No. 3, 1993, pp. 191-211. doi:10.1162/evco.1993.1.3.191
[22] C. Gagné and M. Parizeau, “Genetic Engineering of Hierarchical Fuzzy Regional Representations for Handwritten Character Recognition,” International Journal on Document Analysis and Recognition, Vol. 8, No. 4, 2006, pp. 223-231. doi:10.1007/s10032-005-0005-6
[23] J. P. J. Alberto and C. P. C. Juan, “Genetic Algorithms for Linear Feature Extraction,” Pattern Recognition Letters, Vol. 27, No. 13, 2006, pp. 1508-1514. doi:10.1016/j.patrec.2006.02.011
[24] M. S. William and A. D. J. Kenneth, “An Analysis of Multipoint Crossover,” Proceedings of the First Workshop on Foundation of Genetic Algorithms, Bloomington, 15-18 July 1990, pp. 301-315.
[25] D. Delahaye, J. M. Alliot, M. Schoenauer and J. L. Farges, “Genetic Algorithms for Partitioning Air Space,” Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Application, San Antonio, 1-4 March 1994, pp. 291-297.
[26] G. Menier, “On-Line System of Reading and Writing Cursive Handwriting: Continuous Analysis of Features and Global Interpretation Optimized by Genetic Algorithm,” Ph.D. Thesis, University Rennes 1, Rennes, 1995.
[27] H. L. Sai and K. L. Hak, “Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer Functions,” Journal of Intelligent Learning Systems and Applications, Vol. 3, No. 1, 2011, pp. 37-44. doi:10.4236/jilsa.2011.31005
[28] S. Budi, A. B. Muhammad and E. W. Stefanus, “A Cross Entropy-Genetic Algorithm for M-Machines No-Wait JobShop Scheduling Problem,” Journal of Intelligent Learning Systems and Applications, Vol. 3, No. 3, 2011, pp. 171-180. doi:10.4236/jilsa.2011.33018
[29] Z. Bahad?r and ?. ?brahim, “An Improved Genetic Algorithm for Crew Pairing Optimization,” Journal of Intelligent Learning Systems and Applications, Vol. 4, No. 1, 2012, pp. 70-80. doi:10.4236/jilsa.2012.41007

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