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Determine Characters by Mathematical Model for Segmentation Arabic Words by Voronoi Diagrams

Affiliations

  • School of Computer Science, Faculty of Information Science and Technology, Centre of Artificial Intelligence Universiti Kebangsaan Malaysia, Bangi - 43200, Selangor, Malaysia

Abstract


Objectives: The objectives are to use a mathematical model to define a region-based segmentation method. This study determines whether the Connected Component (CC) is one or more than one character. Method: Whereas the other methods they tend to ignore the solid foundation of describing characters and connection points. This proposed method adopts on many stages for adaptive the mathematic in segmentation characters process are: i) peak detection from vertical histogram for (CC), and ii) enhancement of the model using a mathematical model to improve the segmentation method based on the Voronoi Diagram (VD) Through a number of peaks. Findings: Whereas characters, such as س and ص, are confusing to segmentation methods; these errors include separating connection strokes from both sides to produce a separated one. Other errors must be handled at a later stage, such as segmenting the character ح at an acute angle. Whereas the mathematical model is depending on peaks, numbers, direction, and length of CC. This model is tested on segmentation using five Arabic datasets as: AHDB, IFN-ENIT, AHDB-FTR, APTI, Zeki and Al Hamad DB datasets. The Preliminary results show that the application of the EDMS feature with multi perceptron-NN classifier it’s preferable. Its accuracy when compared with Zeki method is 96.81% for the ACTOR printed dataset and the rate of this method is 85.81% for Zeki dataset and also compared with Al Hamad method is 95.09%, and 89.10% for ACDARhandwrittendataset. Whereas the others datasets accuracies are 95.09% for IFN-ENIT, 98.27% for APTI, 91.63% for AHDB, and 90.69% for AHDB-FTR on same feature (EDMS) and classifier (MLP_NN). Novelty: Adapt Mathematics with segmentation process to determine whether the CC is one or more than one character. Using a mathematical model based on the VD to avoid over segmentation .

Keywords

Mathematical Model, Arabic words, More than one character, Segmentation, Voronoi Diagrams.

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References


  • Zeki A. Segmentation of Arabic characters using Voronoi diagrams. University Kebangsaan Malaysia, Malaysia.; 2008.
  • Ramdan J, Omar K. Comparative Study of Algorithms for Voronoi Diagram Construction on Segmentation of Arabic Hand Writing. Aust J Basic Appl Sci. 2011;5(11):1653–67.
  • Hamad H a Al. Over-segmentation of handwriting Arabic scripts using an efficient heuristic technique. Int Conf Wavelet Anal Pattern Recognit. 2012;180–5.
  • Hamid A, Haraty R. A neuro-heuristic approach for segmenting handwritten Arabic text. Computer Systems and Applications, ACS/IEEE International Conference on. 2001. 2001. p. 110–3.
  • Al-Waily R. A study on preprocessing and syntactic recognition of handwritten Arabic characters. University of Basrah, Iraq; 1989.
  • Pechwitz M, Maddouri SS, Märgner V, Ellouze N, Amiri H. IFN/ENIT-database of handwritten Arabic words. Proc of CIFED. Citeseer; 2002. p. 127–36.
  • Ramdan J, Omar K, Mady A. Arabic Handwriting Data Base for Text Recognition. 2013;2013(Iceei).
  • Hamad HAAL, Hamdi-cherif A. The Arabic Center for Document Analysis and Recognition ( ACDAR ) - Structure and Perspectives. 2010;(1):85–91.
  • Al-ma S, Elliman D, Higgins C. A Data Base for Arabic Handwritten Text Recognition Research. 2004;1(1):117–21.
  • Slimane F, Kanoun S, Alimi AM. Database and Evaluation Protocols for Arabic Printed Text Recognition. 2012.
  • Zeki A, Zakaria M, Liong C. The Use of Area-Voronoi Diagram in Separating Arabic Text Connected Components. 2007.
  • Jyothi J, Manjusha K, Kumar MA, Soman KP. Innovative Feature Sets for Machine Learning based Telugu Character Recognition. Indian J Sci Technol. 2015;8(24).
  • Eli Billauer. Peak detection using MATLAB [Internet]. 2013. Available from: http://www.billauer.co.il
  • Shukla MK, Banka H, Yadav KP. Structural Features Extraction for Devnagari and Bangla Language Documents. Indian J Sci Technol. 2015;8(13).
  • Bataineh B, Abdullah SNHS, Omar K. A statistical global feature extraction method for optical font recognition. Intelligent Information and Database Systems. Springer; 2011. p. 257–67.
  • Thakare V. Survey On Image Texture Classification Techniques. Int J Adv Technol. 2013;4(1):97–104.
  • Zitova B. Image registration methods: a survey. Image Vis Comput. 2003;21(11):977–1000.
  • Zeki AM. The segmentation problem in arabic character recognition the state of the art. Information and Communication Technologies, 2005 ICICT 2005 First International Conference on. IEEE; 2005. p. 11–26.
  • M Zeki A, S Zakaria M, Yeun Liong C. Isolation of dots for arabic ocr using voronoi diagrams. Proceedings of the International Conference on Electrical Engineering and Informatics; 2007.

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