Abstract
This paper describes the design and implementation of a system that identify the writer using off-line Arabic handwriting. Our approach is based on the combination of global and structural features. We used genetic algorithm for feature subset selection in order to eliminate the redundant and irrelevant ones. A modular Multilayer Perceptron (MLP) classifier was used. Experiments have shown writer identification accuracies reach acceptable performance levels with an average rate of 94.73% using optimal feature subset. Experiments are carried on a database of 180 text samples, whose text was made to ensure the involvement of the various internal shapes and letters locations within a word.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Srihari, S.N., Cha, S.H., Arora, H., Lee, S.: Individuality of Handwriting. Journal of Forensic Sciences 47(4), 856–872 (2002)
Cha, S.H., Srihari, S.H.: Multiple Feature Integration for Writer Verification. In: Proceeding of the 7th International Workshop on Frontiers in Handwriting Recognition (IWFHR 2000), Amsterdam, September 2000, pp. 333–342 (2000)
Writer Identification Using Arabic Script. The second International Conference on Machine Intelligence (ACIDCA-ICMI 2005), Tozeur/Tunisia, 1001-1005 (2005)
Korsheed, M.S.M.: Automatic Recognition of words in Arabic manuscripts. PhD thesis, Churchill College University Of Cambridge (2000)
Amara, N., Ellouze, N.: Overview and Advances in Arabic Optical Character Recognition. Asian Journal on Information Technology, Grace Publications Network 3, 217–240 (2004)
Raymer, M., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality Reduc-tion Using Genetic Algorithms. IEEE Transactions on Evol. Comput. 4, 164–171 (2000)
Oliveira, L.S.: A Methodology for Feature Selection Using Multi-objective Genetic Algorithms for Handwritten Digit String. International Journal of Pattern Recognition and Artificial intelligence 17(6), 903–929 (2003)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant Feature and the Subset Selection Problem. In: Eleventh International Conference on Machine learning (ML 1994), New Brunswick (1994)
Gazzah, S., Amara, N.: Utilisation des Ondelettes et des Réseaux de Neurones en Identification des Fontes Arabes. In: Conférence Internationale Signaux, Circuits et Systèmes (SCS 2004), Monastir/Tunisia, pp. 498–502 (2004)
Gazzah, S., Amara, N.: Writer Identification Using SVM Classifier and Genetic Algorithm for Optimal Futures Selection. In: International Arab Conference on Information Technology (ACIT 2005), Amman / Jordan, pp. 461–466 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gazzah, S., Essoukri Ben Amara, N. (2006). Writer Identification Using Modular MLP Classifier and Genetic Algorithm for Optimal Features Selection. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_40
Download citation
DOI: https://doi.org/10.1007/11760023_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)