Classification Performance of Linear Binary Pattern and Histogram Oriented Features for Arabic Characters Images: A Review

There are millions of texts store in both off line and online forms. To utilize these documents properly, there is need of organizing these documents systematically and lots of applications are available for this purpose. Text classification is an important area of image processing deal with how the document belongs to its suitable class or category. Like other languages, Arabic language is also very rich and complex inflectional language which makes Arabic language very complex for ordinary analysis. In this review paper, we focus on the published research, especially in the field of Arabic text classification. Regard these all, three different types of feature extraction techniques are also implemented to extract features from different images of Arabic characters and presents a performance results of these techniques. From the result, it can be concluded that the combination of Linear binary pattern descriptor and Legendre moment, based moments features outperform and increase the accuracy of the LBP classifiers from 91.99 % to 93.12%.

Sungin Behram Khan, Dr. Gulzar Ahmad, Faheem Ali, Farooq Faisal, Irfan Ahmed, Salman Elahi, "Classification Performance of Linear Binary Pattern and Histogram Oriented Features for Arabic Characters Images: A Review" International Journal of Engineering Works, Vol. 5, Issue 4, PP. 56-60, April 2018.

1. [1] Fabrizio Sebastiani, “Machine learning in automated text categorization,” ACM computing surveys (CSUR), 34(1):1–47, 2002.
2. [2] Peter Jackson and Isabelle Moulinier, “Natural language processing for online applications:Text retrieval, extraction and categorization,” volume 5. John  Benjamins Publishing, 2007.
3. [3] AM Mesleh, “ Support vector machine text classifier for arabic articles: Ant colony optimization-based feature subset selection, ”The Arab Academy for Banking and Financial Sciences, 2008.
4. [4] Franca Debole and Fabrizio Sebastiani, “An analysis of the relative hardness of reuters-21578 subsets, ” Journal of the Association for Information Science and Technology,56(6):584–596, 2005.
5. [5] Abdelwadood Mohd Mesleh, “Support vector machines based arabic language text classification system: feature selection comparative study,” In Advances in Computer and Information Sciences and Engineering, pages 11–16.Springer, 2008.       
6. [6] Alaa M El-Halees.Arabic text classification using maximum entropy.”IUG Journa of Natural Studies, 15(1), 2015.
7. [7] Mostafa M Syiam, Zaki T Fayed, and Mena B Habib, “An intelligent system for arabic text Categorization,” International Journal of Intelligent Computing and Information Sciences, 6(1):1–19, 2006.
8. [8] Mohammad S Khorsheed and Abdulmohsen O Al-Thubaity,“ Comparative evaluation of text classification techniques using a large diverse arabic dataset, ” Language resources and evaluation, 47(2):513–538, 2013.
9. [9] Bassam Al-Shargabi, Waseem Al-Romimah, and Fekry Olayah, “A comparative study for arabic text classification algorithms based on stop words elimination, ” In Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications, page 11. ACM, 2011.
10. [10] Lambert Schomaker, Katrin Franke, and Marius Bulacu, “Using codebooks of fragmented. Pattern Recog-nition Letters, ” 28(6):719–727, 2007.
11. [11] Horst Bunke and Kaspar Riesen, “Recent advances in graph-based pattern recognition with applications in document analysis, ”  Pattern Recognition, 44(5):1057–1067, 2011.
12. [12] G¨osta H Granlund, “Fourier preprocessing for hand print character recognition,” IEEE transactions on computers, 100(2):195–201, 1972.
13. [13]H Al-Yousefi and SS Udpa, “Recognition of arabic characters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(8):853–857, 1992.
14. [14] K Roy, A Banerjee, and U Pal, “A system for word-wise handwritten script identification for indian postal automation, ” In India Annual Conference, 2004. Proceedings of the IEEE INDICON 2004. First, pages 266–271. IEEE, 2004.
15. [15] Timo Ojala, Matti Pietik¨ainen, and David Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern recognition, 29(1):51–59, 1996.
16. [16] Timo Ahonen, Abdenour Hadid, and Matti Pietik¨ainen, “Face recognition with local binary Patterns,” Computer vision-eccv 2004, pages 469–481, 2004.
17. [17] Navneet Dalal and Bill Triggs, “Histograms of oriented gradients for human detection,” In Computer Vision and Pattern Recognition, 2005. CVPR2005. IEEE Computer Society Conference on, volume 1, pages 886–893. IEEE,2005.
18. [18] Seung Eun Lee, Kyungwon Min, and Taeweon Suh, “Accelerating histograms of oriented gradients descriptor extraction for pedestrian recognition, ” Computers & Electrical Engineering, 39(4):1043–1048, 2013.
19. [19] Oscar D´eniz, Gloria Bueno, Jes´us Salido, and Fernando De la Torre, “ Face recognition using histograms of oriented gradients, ” Pattern Recognition Letters, 32(12):1598–1603,2011.
20. [20] Jon Arr´ospide, Luis Salgado, and Massimo Camplani,“ Image-based on-road vehicle detection using cost-effective histograms of oriented gradients, ” Journal of Visual Communication and Image Representation, 24(7):1182–1190, 2013.
21. [21] Samir Al-Emami and Mike Usher, “On-line recognition of handwritten arabic characters,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):704–710, 1990.