Touchless Written English Characters Recognition using Neural Network

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends     (IJCOT)          
 
© 2012 by IJCOT Journal
Volume-2 Issue-3                          
Year of Publication : 2012
Authors :  Bikash Chandra Karmokar, M. A. Parvez Mahmud, Md. Kibria Siddiquee, Kawser Wazed Nafi, Tonny Shekha Kar

Citation

Bikash Chandra Karmokar, M. A. Parvez Mahmud, Md. Kibria Siddiquee, Kawser Wazed Nafi, Tonny Shekha Kar "Touchless Written English Characters Recognition using Neural Network" . International Journal of Computer & organization Trends  (IJCOT), V2(3):24-28 May - Jun 2012, ISSN 2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.

Abstract

Touchless written English character recognizer (TER), a new touchless approach to write and an intelligent approach to recognize English characters has been proposed in this paper. In TER, the inputs of English characters have been taken by touchless fashion i.e. by sensing specific color object with a moving hand tracking in front of a webcam. Then they have been recognized by efficient Artificial Neural Network (ANN). Like the application of other traditional computer input devices such as mouse or keyboard, TER can be extended to write and recognize English words and sentences by adding characters one by one to the text editor. Proposed TER has been applied for several different forms of touchless writings, namely 26 English characters and 10 English digits. Here for training, ANN with Scale Conjugate Gradient (SCG) method has been used that converges the training time faster and recognizes with good generalization ability. TER can be useful for the disabled persons.

References

[1] J.Pradeep, E.Srinivasan and S.Himavathi,” Diagonal based feature extraction for handwritten alphabets recognition system using neural network”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 1, Feb 2011.
[2] Anita Pal1 & Dayashankar Singh, “Handwritten English Character Recognition Using Neural Network”, International Journal of Computer Science & CommunicationVol. 1, No. 2, July-December 2010, pp. 141- 144.
[3] U. Pal, and B. B. Chaudhuri, “Indian Script Character Recognition: a Survey”, Pattern Recognition, 37(9): 1887–1899, 2004.
[4] C. Sureshkumar and Dr. T. Ravichandran, “Character recognition using RCS with neural network”, International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010.
[5] Miguel Po-Hsien Wu, “Handwritten Character Recognition”, BSC Thesis, The University of Queensland.
[6] J. Cai and Z. Q. Liu, “Integration of structural and statistical information for unconstrained handwritten numeral recognition”, IEEE Trans. On PAMI, 21(3): 263-270, 1999. [7] R. Plamondon and S. N. Srihari, “On-line and off-line handwritten recognition: A comprehensive survey”, IEEE Trans. on PAMI, 22(1): 62-84, 2000.
[8] P. Wunsch and A. F. Laine, “Wavelet Descriptors for Multi resolution Recognition of Hand-printed Digits”, Pattern Recognition, 28(8):1237- 1226, 1995.
[9] K. Kim and S. Y. Bang, “A handwritten numeral character classification using tolerant Rough set”, IEEE Trans. On PAMI, 22(9): 923-937, 2000.
[10] M. Turk and A. Pentland, “Eigen faces for Recognition”, Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[11] K. Ohba and K. Ikeuchi, “Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 9, pp. 1043-1048, 1997.
[12] H. Murase and S. Nayar, “Visual Learning and Recognition of 3D Objects from Appearance”, International Journal of Computer Vision, vol 14, pp. 5-24, 1995.
[13] M. Mudrova, A. Prochazka,”Principal component analysis in image processing”, Institute of Chemical Technology, Prague.
[14] Md. Saidur Rahman, G.M. Atiqur Rahaman, Asif Ahmed and G.M. Salahuddin, “An approach to recognize handwritten English Numerals for postal automation”, ICCIT 2008.
[15] J. Lunden and V. Koivunen, “Robust estimation of radar pulse modulation,” in Proc. 6th IEEE Int. Symp. on Signal Processing and Inform. Technology, Vancouver, Aug. 27–30, 2006.
[16] Martin Fodslette Moller. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks, 6:525-533, 1993.
[17] Mozer M. C.; Smolensky P., (1989): Skeletonization: A technique for trimming the fat from a network via relevance assessment. in D. S. Touretzky, Advances in Neural Information Processing Systems 1, pp. 107–115, Ed. Denver.
[18] Chakraborty, G.; Chakraborty, B.; “A novel normalization technique for unsupervised learning in ANN”, IEE Transaction on Jan 2000, Volume 11, Issue 1.
[19] Amjad Rehman, Dzulkifli Mohamad and Ghazali Sulong, “Implicit Vs Explicit based Script Segmentation and Recognition: A Performance Comparison on Benchmark Database”, Int. J. Open Problems Compt. Math.,Vol.2,No.3,September 2009.
[20] J. Cai and Z. Q. Liu, “Integration of structural and statistical information for unconstrained handwritten numeral recognition”, IEEE Trans. On PAMI, 21(3): 263-270, 1999

Keywords

Scale Conjugate Gradient, Back Propagation, Principal component analysis, Character recognition. .