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OCR For Printed Urdu Script Using Feed Forward Neural Network

Authors: Inam Shamsher, Zaheer Ahmad, Jehanzeb Khan Orakzai, Awais Adnan


This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. In the proposed system individual characters are recognized using our own proposed method/ algorithms. The feature detection methods are simple and robust. Supervised learning is used to train the feed forward neural network. A prototype of the system has been tested on printed Urdu characters and currently achieves 98.3% character level accuracy on average .Although the system is script/ language independent but we have designed it for Urdu characters only.

Keywords: Algorithm, Feed Forward Neural Networks, Supervised learning, Pattern Matching.

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[1] Y. LeCun, B. Boaer, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, "Handwritten zip code recognition with multilayer networks," International Conference on Pattern Recognition, 1990, pp. 35-44.
[2] K. Fukushlma, T. Imagawa, and E. Ashida, "Character recognition with selective attention ," 1991 International Joint Conference on Neural Networks (I), pp. 593-598.
[3] K. Fukushima and N. Wake,. "Handwritten alphanumeric character recogmtlon by the neocognitron," IEEE 11-mw. on Neurral Networks, Vol. 2, No. 3, May 1991, pp. 355-365.
[4] W. H. Joerding and J. L. Meador, "Encoding a priori information in feedforward networks," Neural Networks, Vol. 4, No. 6, December 1991, pp. 847-856.
[5] J. S. N. Jean and J. Wang, ÔÇÿWeight smoothing to improve network generalization," to appear in IEEE tins. On Neural Networks.
[6] J. Wang and J. S. N. Jean, "Multirexolution neural work for omni font character recognition, "submitted to 1999 IEEE International Conference on Neural Networks.
[7] A. Rajavelu, M. T. Muaavi, and M. V. Shirvaikar, "A neural network approach to character recognition," Neuml Networks, Vol. 2, No. 5, 1989, pp. 387-389.
[8] Y. Hayashi, M. Sakata, T. Nakao, T. Ohno, and S. Ohhashi, "Alphanumeric character recognition using a connectionist model with the pocket algorithm," 1989 International Joint Conference on Neural Networks (II), pp. 606.
[9] S. Kahan, T. Pavlidis, and H. S. Baird, "On the recognition of printed characters of any font and size," IEEE I%ans. on Pattern Analysis and Machine Intelligence, Vol. PAMI-9, No. 2, March 1987, pp. 274288.
[10] C. Wu, J. Wang, and W. Wu, ÔÇÿA shunting multilayer perception network for confusing/composite pattern recognition," Pattern Recognition, Vol. 24, No. 11, 1991, pp. 1093-1103.
[11] M. Maier, ÔÇÿSeparating characters in scripted documents," 1986 International Conference on Pattern Recognition, pp. 1056-1058.
[12] S. Harmalkar and R. M. K. Sinha, "Integrating word level knowledge in text recognition, "1990 International Conference on Pattern Recognition, pp. 758-760.
[13] R. G. Casey and G. Nagy, "Recursive segmentation and classification of composite character pattern," 1982 International Joint Conference on Pattern Recognition, pp. 1023-1026.