Hand Written Recognition Using Modified N_Tuple Method

Authors

  • Fadhil Hanoon Abbood Collage of Education-almustansiriyah university, Iraq
  • Intisar Abid Yousif College of Education Al Mustansiriyah University Iraq
  • Shaimaa Khudhair Salah College of Education Al Mustansiriyah University Iraq

DOI:

https://doi.org/10.31695/IJASRE.2020.33942

Keywords:

Hand Written, N_Tuple, Discrete Wavelet Change

Abstract

In a world running on data, accurate handwriting recognition could become a powerful tool.  With it, hastily scribbled notes and formal, handwritten letters become readable by a computer.

In a writer recognition system, the system searches "one to many in a wide database of manual samples from established authors, and returns a potential list of candidates. A n-tuple method of pattern recognition is developed and evaluated for use with unique references to unconstraint manually written characters for classification of non-deterministic content. A new method is seen to update the system and to increase the implementation and certainty of the findings. The results show that a superior can be achieved and show the legitimacy of the procedures proposed.

References

- Albrecht S., “A Modular Neural Network Architecture with Additional Generalization Abilities for High Dimensional Input Vectors” ,Ph.D. thesis , Manchester Metropolitan University, Department of Computing, September 1996.

- Belanche L., Nebo A.,” INTELLIGENT DATA ANALYSIS AND DATA MINING “ 2001 .

- Carl G. Looney, “Opto-Mechatronic Systems Handbook: Techniques and Applications”, 2002.

- Kolcz. A, Sun.X and Kalita.J , "Efficient Handling of High-Dimensional Feature Spaces by Randomized Classifier Ensembles,", 2003 .

- G. Raju and K. Revathy.”Wavepackets in the Recognition of Isolated Handwritten Characters”, Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 - 4, 2007, London, U.K

- R. S. Stankovic, B. J. Falkowski. ”The Haar wavelet transform: its status and achievements”, Computers and Electrical Engineering 29 (2003) 25– 44.

- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : “A library for support vector machines”. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.

- P. S. A. Chakravarthy, A. S. N., Penmetsa V. Krishna Raja, “Handwritten Text Image Authentication Using Back Propagation,” arXiv Prepr. arXiv, no. 1110.1488, 2011.

- M. N. Abdi and M. Khemakhem, “Arabic writer identification and verification using template matching analysis of texture,” Proc. - 2012 IEEE 12th Int. Conf. Comput. Inf. Technol. CIT 2012, no. December, pp. 592–597, 2012.

- Y. Hannad, I. Siddiqi, and M. E. Y. El Kettani, “Writer identification using texture descriptors of handwritten fragments,” Expert Syst. Appl., vol. 47, pp. 14–22, 2016.

- Y. Hannad, I. Siddiqi, Y. El Merabet, and M. El Youssfi El Kettani, “Arabic writer identification system using the histogram of oriented gradients (HOG) of handwritten fragments,” in Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence, 2016, pp. 98–102.

- H. Sheikh, A., Khotanlou, “Writer identity recognition and confirmation using persian handwritten texts,” Int. J. Adv. Appl. Sci., vol. 6, no. 2, pp. 98–105, 2017.

- J. H. AlKhateeb, F. Khelifi, J. Jiang, and S. S. Ipson, “A new approach for off-line handwritten Arabic word recognition using KNN classifier,” in 2009 IEEE International Conference on Signal and Image Processing Applications, 2009, pp. 191–194.

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How to Cite

Fadhil Hanoon Abbood, Intisar Abid Yousif, & Shaimaa Khudhair Salah. (2020). Hand Written Recognition Using Modified N_Tuple Method. International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE, 6(12), 102–109. https://doi.org/10.31695/IJASRE.2020.33942

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