Abstract
Knowledge concerning the topography of Arabic letters, as well as the structural characteristics between background regions and character components is investigated as a novel approach for Arabic recognition. The suggested feature extraction method reduces the classifier input data to only the most significant and essential.
First, connected components consisting of more than one character are segmented into characters. Secondly, the primitives are extracted according to the knowledge of character structures and some statistical characteristics. Finally a hybrid model based on the combination of support vector machines (SVM) classifier and particle swarm optimization (PSO) is used to evaluate the performance of features extracted.
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
References
Almuallim, H., Yamaguchi, S.: A method of recognition of Arabic cursive handwriting. IEEE Trans. Pattern Anal. Mach. Intell. 5, 715–722 (1987)
Clocksin, W.F.: Towards automatic transcription of Syriac handwriting. In: Proceedings of the 12th International Conference on Image Analysis and Processing, pp. 664–669. IEEE, September 2003
Mozaffari, S., Faez, K., Ziaratban, M.: Structural decomposition and statistical description of Farsi/Arabic handwritten numeric characters. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 237–241. IEEE, August 2005
Abdleazeem, S., El-Sherif, E.: Arabic handwritten digit recognition. Int. J. Doc. Anal. Recogn. 11(3), 127–141 (2008)
Alaei, A., Nagabhushan, P., Pal, U.: Fine classification of unconstrained handwritten Persian/Arabic numerals by removing confusion amongst similar classes. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2009, pp. 601–605. IEEE, July 2009
Alamri, H., He, C., Suen, C.: A new approach for segmentation and recognition of Arabic handwritten touching numeral pairs. In: Computer Analysis of Images and Patterns, pp. 165–172. Springer, Heidelberg (2009)
Awaidah, S.M., Mahmoud, S.A.: A multiple feature/resolution scheme to Arabic (Indian) numerals recognition using hidden Markov models. Sig. Process. 89(6), 1176–1184 (2009)
Amara, M., Zidi, K.: New mechanisms to enhance the performances of arabic text recognition system: feature selection. In: Handbook of Research on Machine Learning Innovations and Trends, pp. 879–896. IGI Global (2017)
AlKhateeb, J.H., Jiang, J., Ren, J., Khelifi, F., Ipson, S.S.: Multiclass classification of unconstrained handwritten Arabic words using machine learning approaches. Open Sig. Process. J. 2, 21–28 (2009)
Elzobi, M., Al-Hamadi, A., Saeed, A., Dings, L.: Arabic handwriting recognition using Gabor wavelet transform and SVM. In: 2012 IEEE 11th International Conference on Signal Processing (ICSP), vol. 3, pp. 2154–2158. IEEE, October 2012
Lawgali, A.: Handwritten digit recognition based on DWT and DCT. Int. J. Database Theory Appl. 8(5), 215–222 (2015)
ElAdel, A., Ejbali, R., Zaied, M., Amar, C.B.: Dyadic multi-resolution analysis-based deep learning for Arabic handwritten character classification. In: 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 807–812. IEEE, November 2015
Mukhopadhyay, P., Chaudhuri, B.B.: A survey of Hough Transform. Pattern Recogn. 48(3), 993–1010 (2015)
Hu, A.K.: Pattern recognition by moment invariants. Proc. IRE 49, 1428 (1961)
Amara, M., Zidi, K., Ghedira, K., Zidi, S.: New rules to enhance the performances of Histogram projection for segmenting small-sized Arabic words. In: Hybrid Intelligent Systems, pp. 167–176. Springer International Publishing (2016)
Xiao, X., Leedham, G.: Knowledge-based English cursive script segmentation. Pattern Recogn. Lett. 21(10), 945–954 (2000)
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H.: IFN/ENIT-database of handwritten Arabic words. In: Proceedings of CIFED, vol. 2, pp. 127–136, October 2002
Amara, M., Zidi, K.: Feature selection using a neuro-genetic approach for Arabic text recognition. In: Metaheuristics and Nature Inspired Computing (2012)
Amara, M., Zidi, K., Ghedira, K.: Towards a generic M-SVM parameters estimation using overlapping swarm intelligence for handwritten characters recognition. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 498–509. Springer International Publishing, October 2016
Amara, M., Zidi, K., Zidi, S., Ghedira, K.: Arabic character recognition based M-SVM: review. In: Advanced Machine Learning Technologies and Applications, pp. 18–25. Springer International Publishing (2014)
Amara, M., Ghedira, K., Zidi, K., Zidi, S.: A comparative study of multi-class support vector machine methods for Arabic characters recognition. In: International Conference on Computer Systems and Applications (2015)
Acknowledgment
This research and innovation work is carried out within a MOBIDOC thesis funded by the EU under the PASRI project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Amara, M., Zidi, K., Ghedira, K. (2020). Structural and Statistical Feature Extraction Methodology for the Recognition of Handwritten Arabic Words. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-14347-3_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14346-6
Online ISBN: 978-3-030-14347-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)