Summary
This article proposes an un-constrained recognition approach for the handwritten Arabic script. The approach starts by explicitly segment each word image into its constituent letters, then a filter-bank of Gabor wavelet transform is used to extract feature vectors corresponding to different scales and orientation in the segmented image. Classification is carried out by employing a support vectors machine algorithm, where IESK-arDB and IFN/ENIT databases are used for testing and evaluation of the proposed approach respectively. A Leave-one-out estimation strategy is followed to assess performance, where results confirmed the approach efficiency.
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.
References
Abuhaiba, I.S.I., Holt, M.J.J., Datta, S.: Recognition of off-line cursive handwriting. Computer Vision and Image Understanding 71(1), 19–38 (1998)
Al-Badr, B.H.: A segmentation-free approach to text recognition with application to Arabic text. Ph.D. thesis, Seattle, WA, USA (1995)
Al-Hajj Mohamad, R., Likforman-Sulem, L., Mokbel, C.: Combining slanted-frame classifiers for improved hmm-based arabic handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(7), 1165–1177 (2009)
Almuallim, H., Yamaguchi, S.: A method of recognition of arabic cursive handwriting. IEEE Trans. Pattern Anal. Mach. Intell. 9, 715–722 (1987)
Atici, A.A., Yarman-Vural, F.T.: A heuristic algorithm for optical character recognition of arabic script. Signal Processing 62(1), 87–99 (1997)
Bushofa, B.: Segmentation and recognition of arabic characters by structural classification. Image and Vision Computing 15(3), 167–179 (1997)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chen, J., Cao, H., Prasad, R., Bhardwaj, A., Natarajan, P.: Gabor features for offline arabic handwriting recognition. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS 2010, pp. 53–58. ACM, New York (2010)
Elzobi, M., Al-Hamadi, A., Al Aghbari, Z., Dings, L.: Iesk-ardb: a database for handwritten arabic and an optimized topological segmentation approach. International Journal on Document Analysis and Recognition (IJDAR), 1–14 (2012)
Haboubi, S., Maddouri, S., Ellouze, N., El-Abed, H.: Invariant primitives for handwritten arabic script: A contrastive study of four feature sets. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2009, pp. 691–697 (2009)
Hamamoto, Y., Uchimura, S., Watanabe, M., Yasuda, T., Mitani, Y., Tomita, S.: A gabor filter-based method for recognizing handwritten numerals. Pattern Recognition 31(4), 395–400 (1998)
Lorigo, L., Govindaraju, V.: Offline arabic handwriting recognition: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 712–724 (2006)
Madhvanath, S., Govindaraju, V.: The role of holistic paradigms in handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 149–164 (2001)
Pechwitz, M., Maergner, V.: Hmm based approach for handwritten arabic word recognition using the ifn/enit - database. In: Proceedings. Seventh International Conference on Document Analysis and Recognition, pp. 890–894 (2003)
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H.: Ifn/enit - database of handwritten arabic words. In: Proc. of CIFED 2002, Tunis, pp. 129–136 (2002)
Xiu, P., Peng, L., Ding, X.-q., Wang, H.: Offline Handwritten Arabic Character Segmentation with Probabilistic Model. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 402–412. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Elzobi, M., Al-Hamadi, A., Al Aghbari, Z., Dings, L., Saeed, A. (2014). Gabor Wavelet Recognition Approach for Off-Line Handwritten Arabic Using Explicit Segmentation. In: S. Choras, R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol 233. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01622-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-01622-1_29
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-01621-4
Online ISBN: 978-3-319-01622-1
eBook Packages: EngineeringEngineering (R0)