An Idea to Recognition of handwritten Characters using Genetic Algorithms


  • Goyal SJ Amity University, Madhya Pradesh
  • Goyal R Amity University, Madhya Pradesh


Machine recognition, Handwriting recognition, neural networks, generic algorithms, graph theory, coordinate geometry, offline handwriting recognition


Challenges in handwritten characters recognition is due to the variation and distortion of handwritten characters, since different people use different style and way of draw the same shape of the characters. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate new or unique styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing.


J.Pradeep, E.Srinivasanand, S.Himavathi, “Diagonal based feature extraction for handwritten alphabets recognition system using neural network,” International Journal of Science & Information Technology (IJCSIT), vol. 3, no. 1, pp. 27-37, Feb 2011.

Nafiz Arica and Fatos T. Yarman-Vural, “An overview of character recognition focused on off-line handwriting,” IEEE transactions on systems, man, and cybernetics-part c: applications and reviews, vol. 31, no. 2, pp. 216-233, May 2001.

M. D. Garris, R. A. Wilkinson, and C. L. Wilson, “Methods for enhancing neural network handwritten character recognition,” International Joint Conference on Neural Networks, IEEE, Seattle, vol. 1, pp. 695-699, July 1991.

R. Plamondon and S.N. Srihari, “On-line and off-line handwriting recognition: A comprehensive survey,” IEEE transactions Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, Jan. 2000.

Lera, G. and Pinzolas, M., “Neighborhood based Levenberg-Marquardt algorithm for neural network training,” IEEE Transaction on Neural Networks, vol. 13, no.5, pp. 1200-1203, Sept 2002.

Satish Kumar and Chandan Singh, “A study of zernike moments and its use in devnagari handwritten character recognition,” International Conference on Cognition and Recognition, pp. 514-520, 2005.

Cheng-Lin Liu, “Normalization-cooperated gradient feature extraction for handwritten character recognition,” IEEE Transaction on pattern analysis and machine intelligence, vol. 29, no. 8, Aug. 2007.

Ján Dolinský and Hideyuki Takagi, “Analysis and modelling of naturalness in handwritten characters,” IEEE transactions on neural networks, vol. 20, no. 10, pp. 1540-51, October 2009.

E.Lecolinet and R.G. Casey, “A survey of methods and strategies in character segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no.7, pp. 690-706, July 1996.

Cheng-Lin Liu, “Normalization-cooperated gradient feature extraction for handwritten character recognition,” IEEE Transaction on pattern analysis and machine intelligence, vol. 29, no. 8, Aug. 2007.

Anita Pal and Dayashankar Singh, “Handwritten english character recognition using neural network,” International Journal of Computer Science & Communication vol. 1, no. 2, pp. 141-144, July-December 2010.

Chang-Lin Liu, “Normalization-cooperated Gradient Feature Extraction for Handwritten character recognition.” IEEE transactions on Pattern Analysis and machine intelligence, vol. -29, Issue8, pp. 1465-1469, Aug 2007.

Walid A. Salameh and Mohammed A. Otair, “Online handwritten character recognition using an optical back propagation neural network,” Issues in Informing Science and Information Technology, vol. 3, pp. 787-795, 2005.

Maier, H. R. and Dandy, G. C., “The effect of internal parameters and geometry on the performance of back propagation neural networks: an empirical study,” Environmental Modelling and Software, Vol. 13, no. 2: pp. 193-209, 1998.

Anil K. Jain and Torfinn Taxt, “Feature extraction methods for character recognition-A survey,” Pattern Recognition, vol. 29, no. 4, pp. 641-662, April 1996.

James A. Freeman, David M. Skapura, “Backpropagation algorithm,” in Neural Networks: Algorithms, Applications, and Programming Techniques (Computation and Neural System), Addison-Wesley Pub (Sd), 1991.

Dougherty, E.R., “Random processes for image and signal processing,” IEEE Press, New York, 2000.

Azizah Suliman, Mohd. Nasir Sulaiman, Mohamed Othman, Rahmita Wirza, “Chain coding and pre processing stages of handwritten character image file,” Electronic Journal of Computer Science and Information Technology (eJCSIT), vol. 2, no. 1, pp. 6-13, 2010.

Dayashankar Singh, Maitree Dutta and Sanajay Kumar Singh “Handwritten character recognition using twelve directional feature input and Neural Network.”International journal of computer applications. (0975-8887) volNo.3, 2010.

Shuchita Upadhyaya et. al, “Handwritten Character Recognition Using Diagonal Based Feature Extraction and Neural Network”, National Conference on Advanced Computing Technology, vol-No. 2, pp. 615-618, March 2013.

Rafael C.Gozalez,Richard E. woods and Steven L.Eddins,Digital Image Processing using MATLAB, Pearson Education, Dorling Kindersley, SouthAsia,2004




How to Cite

Goyal, S. J., & Goyal, R. (2024). An Idea to Recognition of handwritten Characters using Genetic Algorithms. COMPUSOFT: An International Journal of Advanced Computer Technology, 4(04), 1686–1689. Retrieved from



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