The Segmentation of Printed Arabic Characters Based on Interest Point

Authors

  • Fitriyatul Qomariyah Faculty of Computer Science, Brawijaya University, Indonesia
  • Fitri Utaminingrum Faculty of Computer Science, Brawijaya University, Indonesia
  • Wayan Firdaus Mahmudy Faculty of Computer Science, Brawijaya University, Indonesia

Keywords:

Image Segmentation, Connected Arabic Characters Segmentation, Interest Point,

Abstract

Arabic characters are different compared to the other characters whether from their forms or the way they are read. Before conducting a recognition process, we should conduct segmentation or divide each character to identify each Arabic character of the word. The enormous problem of segmenting the connected Arabic characters is dividing each character with different positions, forms, and sizes for each character. Therefore, we suggested a method in segmentation process by using the interesting point, which successfully obtains the 86.5% average accuracy.

References

K. Mohammad, M. Ayyesh, A. Qaroush, and I. Tumar, “Printed Arabic optical character segmentation,” SPIE/IS&T Electron. Imaging, vol. 9399, p. 939911, 2015.

Tihao Chiang and Ya-Qin Zhang, “A new rate control scheme using quadratic rate distortion model,” IEEE Trans. Circuits Syst. Video Technol., vol. 7, no. 1, pp. 246–250, 1997.

S. Shastry, G. Gunasheela, T. Dutt, D. S. Vinay, and S. R. Rupanagudi, “‘i’ — A novel algorithm for optical character recognition (OCR),” 2013 Int. Mutli-Conference Autom. Comput. Commun. Control Compress. Sens., pp. 389–393, 2013.

S. Wshah, Z. Shi, and V. Govindaraju, “Segmentation of Arabic handwriting based on both contour and skeleton segmentation,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, pp. 793–797, 2009.

Y. K. Chen and J. F. Wang, “Segmentation of single- or multipletouching handwritten numeral string using background and foreground analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 11, pp. 1304–1317, 2000.

W. Xianghui, M. Shaoping, and J. Yijiang, “Segmentation of connected Chinese characters based on genetic algorithm,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, vol. 2005, no. 60223004, pp. 645–649, 2005.

R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2, pp. 264–271, 2003.

C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Procedings Alvey Vis. Conf. 1988, pp. 147–151, 1988.

T. Kadir and J. M. Brady, “Scale, Saliency and Image Description,” Int. J. Comput. Vis., vol. 45, no. 2, pp. 83–105, 2001.

K. Mikolajczyk and C. Schmid, “Scale & affine invariant interest point detectors,” Int. J. Comput. Vis., vol. 60, no. 1, pp. 63–86, 2004.

N. Sebe, T. Gevers, J. Van De Weijer, and S. Dijkstra, “Corner detectors for affine invariant salient regions: Is color important?,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4071 LNCS, no. Section 4, pp. 61–71, 2006.

M. S. Karis et al., “Fruit Sorting Based on Machine Vision Technique,” vol. 8, no. 4, pp. 31–35, 1843.

W. A. Mustafa and H. Yazid, “Background Correction using Average Filtering and Gradient Based Thresholding,” vol. 8, no. 5, pp. 81–88, 2016.

D. Kocharyan, “A Modified fingerprint image thinning algorithm,” vol. 32, no. 1, pp. 13–18, 2013.

R. M. Haralick, “A Fast Parallel Algorithm for Thinning Digital Patterns,” vol. 27, no. 3, pp. 236–239, 1984.

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Published

2017-09-01

How to Cite

Qomariyah, F., Utaminingrum, F., & Mahmudy, W. F. (2017). The Segmentation of Printed Arabic Characters Based on Interest Point. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-8), 19–24. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2622

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