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English Sentence Recognition using Artificial Neural Network through Mouse-based Gestures
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 61 - Number 17 |
| Year of Publication: 2013 |
| Authors: Firoj Parwej |
10.5120/10023-4998
|
Firoj Parwej . English Sentence Recognition using Artificial Neural Network through Mouse-based Gestures. International Journal of Computer Applications. 61, 17 ( January 2013), 33-38. DOI=10.5120/10023-4998
Abstract
Handwriting is one of the most important means of daily communication. Although the problem of handwriting recognition has been considered for more than 60 years there are still many open issues, especially in the task of unconstrained handwritten sentence recognition. This paper focuses on the automatic system that recognizes continuous English sentence through a mouse-based gestures in real-time based on Artificial Neural Network. The proposed Artificial Neural Network is trained using the traditional backpropagation algorithm for self supervised neural network which provides the system with great learning ability and thus has proven highly successful in training for feed-forward Artificial Neural Network. The designed algorithm is not only capable of translating discrete gesture moves, but also continuous gestures through the mouse. In this paper we are using the efficient neural network approach for recognizing English sentence drawn by mouse. This approach shows an efficient way of extracting the boundary of the English Sentence and specifies the area of the recognition English sentence where it has been drawn in an image and then used Artificial Neural Network to recognize the English sentence. The proposed approach English sentence recognition (ESR) system is designed and tested successfully. Experimental results show that the higher speed and accuracy were examined.
References
- R. Plamondon and S. N. Srihari. , 2000, "Online and off-line handwriting recognition: a comprehensive survey. ", Pattern Analysis and Machine Intelligence, IEEE Transac-tions on, vol. 22(1), pp 63–84.
- A. L. Bianne-Bernard, F. Menasri, R. A. H. Mohamad, C. Mokbel, C. Kermorvant, and L. Likforman-Sulem. , 2011 , " Dynamic and contextual information in hmm modeling for handwritten word recognition", Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 33(10), pp 2066–2080.
- T. Yang, Y. Xu , 1994, "Hidden Markov Model for Gesture Recognition", CMU-RI-TR-94 10 , The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania, pp 1-23.
- Yusuf Perwej , November 2012, " Recurrent Neural Network Method in Arabic Words Recognition System" ,International Journal of Computer Science and Telecommunications (IJCST), published by Sysbase Solution (Ltd), UK, London (http://www. ijcst. org) , vol. 3, Issue 11, pp 43-48.
- Baugh, C. Albert , and C. Thomas, 1978, "A History of the English Language", 2nd ed. Englewood Cliffs, New Jersey: Prentice-Hall.
- Algeo, and John, 2010, "The Origins and Development of the English Language", Boston, MA: Wadsworth. pp. 182-187.
- M. Schuster and K. K. Paliwal, November 1997 , "Bidirectional recurrent neural networks", IEEE Transactions on Signal Processing,vol 45, pp 2673–2681.
- L. Fausett , 1994, "Fundamentals of Neural Networks" , Prentice Hall, Englewood Cliffs, New Jersey.
- N. Arica, and Yarman Vural, F. T. , 2001, "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.
- S. Jaeger, S. Manke, J. Reichert, and A. Waibel, 2001, "Online handwriting recognition: the NPen++ recognizer", Int. Journal on Document Analysis and Recognition, vol. 3(3) , pp169–180 .
- M. Liwicki and H. Bunke , 2006, "HMM-based on-line recognition of handwritten whiteboard notes", In Proc. 10th Int. Workshop on Frontiers in Handwriting Recognition, pp 595–599.
- O. D. Trier, A. K. Jain and T. Taxt, 1996, "Feature Extraction Methods for Character Recognition A Survey", Pattern Recognition, vol. 29, pp. 641-662.
- S. V. Rajashekararadhya, and P. VanajaRanjan , 2005, "Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south-Indian scripts", Journal of Theoretical and Applied Information Technology, pp 1171- 1181
- A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber, May 2008, "A Novel Connectionist System for Unconstrained Handwriting Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 855–868 .
- M. Keller, H. Tahani, 1992 , "Backpropagation neural network for fuzzy logic" , Inform. Sci. , vol. 62 , pp 205-221.
- S. Abid , F. Fnaiech , M. Najim , (2001), "A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm," IEEE Trans. Neural Networks , vol. 12, pp 424-430 .
- M. Zhihong , H. Ren Wu, S. Liu, X. Yu , 2006 , " A new adaptive backpropagation algorithm based on Lyapunov stability thory for neural networks", IEEE Transactions on Neural Networks , vol. 17, pp 1580-1591 .
- Yahya H. Zweiri, 2006, " Optimization of a three term backpropagation algorithm used for neural network learning ", International journal of Computational Intelligence, vol. 3, pp 322 – 327.
- K. Hornik, M. Stinchcombe, and H. White, 1989, "Multilayer feedforward networks are universal approximators," Neural Netw. , vol. 2, pp. 359-366 .
- A. Krenker , M. Volk, U. Sedlar , J. Bešter , and A. Kos, Feb 2009, "Bidirectional artificial neural networks for mobile-phone fraud detection", ETRI Journal , vol. 31, no. 1, pp. 92-94.
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