Spatial and Textural Aspects for Arabic Handwritten Characters Recognition
DOI:
https://doi.org/10.9781/ijimai.2017.12.002Keywords:
Arabic Documents, Handwritten Character Recognition, Local Binary Patterns, Modified Bitmap SamplingAbstract
The purpose of the present paper is the recognition of handwritten Arabic characters in their isolated form. The specificity of Arabic characters is taken into consideration, each of the proposed feature extraction method integrates one of the two aspects: spatial and textural. In the first step, a modified Bitmap Sampling method is proposed, which converts the character’s images into a binary Matrix and then constructs a Mask for each class. A matching rate is used between the input binary matrix and the masks to determinate the corresponding class. In the second step we investigate the use of an Artificial Neural Network as classifier with the binary matrices as features and then the histograms of Local Binary Patterns to capture the texture aspect of the characters. Finally, the results of these two methods are combined to take into consideration both aspects at the same time. Tested on the Arabic set of the Isolated Farsi Handwritten Character Database, the proposed method has 2.82% error rate.Downloads
References
Y. Boulid, A. Souhar, and M. Y. Elkettani, “Handwritten character recognition based on the specificity and the singularity of the Arabic language,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 4, pp. 45–53, 2017.
J. Cai and Z.-Q. Liu, “Integration of structural and statistical information for unconstrained handwritten numeral recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 3, pp. 263–270, 1999.
L. Heutte, T. Paquet, J.-V. Moreau, Y. Lecourtier, and C. Olivier, “A structural/statistical feature based vector for handwritten character recognition,” Pattern Recognition Letters, vol. 19, no. 7, pp. 629–641, 1998.
M. T. Parvez and S. A. Mahmoud, “Arabic handwriting recognition using structural and syntactic pattern attributes,” Pattern Recognition, vol. 46, no. 1, pp. 141–154, 2013.
A. Amin, “Recognition of hand-printed characters based on structural description and inductive logic programming,” Pattern Recognition Letters, vol. 24, no. 16, pp. 3187–3196, 2003.
A. Sahlol and C. Suen, “A novel method for the recognition of isolated handwritten Arabic characters,” arXiv preprint arXiv:1402.6650, 2014.
G. Abandah and N. Anssari, “Novel moment features extraction for recognizing handwritten Arabic letters,” Journal of Computer Science, vol. 5, no. 3, p. 226, 2009.
A. Asiri and M. S. Khorsheed, “Automatic processing of handwritten Arabic forms using neural networks,” in IEC (Prague), 2005, pp. 313–317.
J. Shanbehzadeh, H. Pezashki, and A. Sarrafzadeh, “Features extraction from Farsi hand written letters,” in Proceedings of Image and Vision Computing, 2007, pp. 35–40.
F. Bouchareb, R. Hamdi, and M. Bedda, “Handwritten Arabic character recognition based on SVM classifier,” in Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA 2008), IEEE, 2008, pp. 1–4.
M. Torki, M. E. Hussein, A. Elsallamy, M. Fayyaz, and S. Yaser, “Window-based descriptors for Arabic handwritten alphabet recognition: A comparative study on a novel dataset,” arXiv preprint arXiv:1411.3519, 2014.
M. Rajabi, N. Nematbakhsh, and S. A. Monadjemi, “A new decision tree for recognition of Persian handwritten characters,” International Journal of Computer Applications, vol. 44, no. 6, pp. 52–58, 2012.
A. Alaei, P. Nagabhushan, and U. Pal, “A new two-stage scheme for the recognition of Persian handwritten characters,” in Proceedings of the 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, 2010, pp. 130–135.
A. Alaei, U. Pal, and P. Nagabhushan, “A comparative study of Persian/Arabic handwritten character recognition,” in Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, 2012, pp. 123–128.
S. Mozaffari and H. Soltanizadeh, “ICDAR 2009 handwritten Farsi/Arabic character recognition competition,” in Proceedings of the 2009 10th International Conference on Document Analysis and Recognition (ICDAR’09), IEEE, 2009, pp. 1413–1417.
A. Lawgali, A. Bouridane, M. Angelova, and Z. Ghassemlooy, “Handwritten Arabic character recognition: Which feature extraction method?” International Journal of Advanced Science and Technology, vol. 34, pp. 1–8, 2011.
N. H. Hammad and M. E. Musa, “The impact of dots representation in recognition of isolated Arabic characters,” International Journal of Information Engineering and Electronic Business, vol. 8, no. 6, p. 37, 2016.
A. A. Ewees, A. T. Sahlol, and M. A. Amasha, “A bio-inspired moth-flame optimization algorithm for Arabic handwritten letter recognition,” in Proceedings of the International Conference on Control, Artificial Intelligence, Robotics and Optimization (ICCAIRO 2017), 2017.
S. Mozaffari, K. Faez, F. Faradji, M. Ziaratban, and S. M. Golzan, “A comprehensive isolated Farsi/Arabic character database for handwritten OCR research,” in Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition, Suvisoft, 2006.
T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
S. Tulyakov, S. Jaeger, V. Govindaraju, and D. Doermann, “Review of classifier combination methods,” in Machine Learning in Document Analysis and Recognition, Springer, 2008, pp. 361–386.
M. Askari, M. Asadi, A. Asilian Bidgoli, and H. Ebrahimpour, “Isolated Persian/Arabic handwriting characters: Derivative projection profile features, implemented on GPUs,” Journal of AI and Data Mining, vol. 4, no. 1, pp. 9–17, 2016.
Downloads
Published
-
Abstract49
-
PDF35
Submit Article
Information
