Neural Network Based Handwritten Character Recognition
DOI:
https://doi.org/10.32628/CSEIT217472Keywords:
Handwritten character recognition, Neural NetworkAbstract
In this paper, an endeavor is made to perceive handwritten characters for English letters in order. The principle point of this task is to plan a master framework for, "HCR(English) utilizing Neural Network". that can viably perceive a specific character of type design utilizing the Artificial Neural Network approach. The handwritten character acknowledgment issue has become the most well-known issue in AI. Handwritten character acknowledgment has been a difficult space of examination, with the execution of Machine Learning we propose a Neural Network based methodology. Acknowledgment, precision rate, execution and execution time are a significant model that will be met by the technique being utilized.
References
- S. Mori, C.Y. Suen and K. Kamamoto, “Historical review of OCR research and development,” Proc. of IEEE, vol. 80, pp. 1029- 1058, July 1992.
- S. Impedovo, L. Ottaviano and S. Occhinegro, “Optical character recognition”, International Journal Pattern Recognition and Artificial Intelligence, Vol. 5(1-2), pp. 1-24, 1991.
- V.K. Govindan and A.P. Shivaprasad, “Character Recognition – A review,” Pattern Recognition, vol. 23, no. 7, pp. 671- 683, 1990
- R. Plamondon and S. N. Srihari, “On-line and off- line handwritten character recognition: A comprehensive survey,”IEEE. Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, no. 1, pp. 63-84, 2000.
- U. Bhattacharya, and B. B. Chaudhuri, “Handwritten numeral databases of Indian scripts and multistage recognition of mixed 6978-1-4244-9391-3/11/426.00© 2011 IEEE 45
- Abdulllah, M., Agal, A., Alharthi, M., & Alrashidi, M. (2018). Retracted: Arabic handwriting recognition using neural network
- Hertz, J. A. (2018). Introduction to the theory of neural computation. Boca Raton: CRC Press.
- Hamid, N. A., & Sjarif, N. N. A. (2017). Handwritten recognition using SVM, KNN and neural network. arXiv preprint arXiv:1702.00723.
- Kumar, P., Saini, R., Roy, P. P., & Pal, U. (2018). A lexicon-free approach for 3D handwriting recognition using classifier combination. Pattern Recognition Letters, 103, 1- 7.
- Kurková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., & Maglogiannis, I. (2018).
- Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings. Basingstoke, England: Springer.
- Larasati, R., & KeungLam, H. (2017, November). Handwritten digits recognition using ensemble neural networks and ensemble decision tree. In 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS) (pp. 99-104). IEEE.
- Lee, J. H., Shin, J., & Realff, M. J. (2018). Machine learning: Overview of the recent progresses and implications for the process systems engineering field. Computers & Chemical Engineering, 114, 111-121.
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