Handwritten character recognition using diagonal feature extraction method and MLFFN having back propagation algorithm
- 1Dept. of Computer Science & Engineering, Institute of Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, CG, India
- 2Dept. of Computer Science & Engineering, Institute of Technology, Guru Ghasidas Vishwavidyalaya, Central University, Bilaspur, CG, India
Res. J. Computer & IT Sci., Volume 6, Issue (5), Pages 1-3, July,20 (2018)
This paper is a new work of authors in the field of Handwritten Digital Recognition. The authors had proposed a methodology for extraction of handwritten characters. The characters are from the English Language. The paper tries to give a way to do the work. It also shows a brief description of the work done in the field of Feature Extraction. The major emphasis of the paper is on the algorithm for feature extraction and then the topology and learning methodology used for classification.
- Singh V.K. (2015)., One Solution to XOR problem using Multilayer Perceptron having Minimum Configuration., International Journal of Science and Engineering (IJSE), ISSN: 2347-2200, 3(2), 32-41.
- Singh V.K. (2015)., Two Solutions to the XOR problem using minimum configuration MLP., International Journal of Advanced Engineering Science and Technological Research (IJAESTR), ISSN: 2321-1202, 3(3), 16-20.
- Singh V.K. (2016)., Proposing Solution to XOR problem using minimum configuration MLP., Procedia Computer Science by Elsevier of International Conference on Computational Modeling and Security (CMS 2016), R.L. Jalappa Institute of Technology, Bangalore, Karnataka, India, ISSN: 1877-0509, 85, 263-270.
- Singh V.K. (2016)., Mathematical Explanation To Solution For Ex-NOR Problem Using MLFFN., International Journal of Information Sciences and Techniques (IJIST), 6(1), 105-122.
- Singh V.K. (2016)., ANN Implementation of Constructing Logic Gates Focusing On Ex-NOR., Research Journal of Computer and Information Technology Sciences, E-ISSN 2320-6527, 4(6), 1-11.
- Singh V.K. (2016)., Mathematical Analysis For Training ANNs Using Basic Learning Algorithms., Research Journal of Computer and Information Technology Sciences, E-ISSN 2320-6527, 4(7), 6-13.
- Singh V.K. and Pandey S. (2016)., Minimum Configuration MLP for Solving XOR Problem., Proceeding of the 10th INDIACom-2016, IEEE Conference ID:37465, International Conference on Computing for Sustainable Global Development, Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India, ISSN: 0973-7529, ISBN: 978-93-80544-20-5, 168-173.
- Singh V.K. (2016)., Proposing an Ex-NOR Solutions Using ANN., Proceeding of International Conference on Information, Communication and Computing Technology (ICICCT-2016), ISBN: 978-93-85777-66-0, IIC, New Delhi, Jagan Institute of Management Studies and CSI.
- Singh V.K. (2016)., Proposing a New ANN model for solving XNOR problem., In System Modeling & Advancement in Research Trends (SMART), International Conference, 32-36.
- Patel C.I., Patel R. and Patel P. (2011)., Handwritten character recognition using neural network., International Journal of Scientific & Engineering Research, 2(5), 1-6.
- Prasad K., Nigam D.C., Lakhotiya A. and Umre D. (2013)., Character recognition using matlab's neural network toolbox., International Journal of u-and e-Service, Science and Technology, 6(1), 13-20.
- Aggarwal A., Rani R. and Dhir R. (2012)., Handwritten Devanagari character recognition using Gradient features., International Journal of Advanced Research in Computer Science and Software Engineering, 2(5), 85-90.
- Hammandlu M., Murali K.R. and Kumar H. (2011)., Neural based Handwritten Character Recognition., Advances in Computing, 1(1), 18-23.
- Gaurav D.D. and Ramesh R. (2012)., A feature extraction technique based on character geometry for character recognition., arXiv preprint arXiv:1202.3884.
- Wang S. (2011)., A review of gradient-based and edge-based feature extraction methods for object detection., In Computer and Information Technology (CIT), IEEE 11th International Conference on, 277-282.