Neural Network Based Handwritten Character Recognition

Authors

  • Monika  M.Tech Scholar, Computer Science and Engineering, RTMNU, Nagpur, Maharashtra, India
  • Monika Ingole  M.Tech Scholar, Computer Science and Engineering, RTMNU, Nagpur, Maharashtra, India
  • Khemutai Tighare  Assistant Professor, Computer Science & Engineering, RTMNU, Nagpur, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT217472

Keywords:

Handwritten character recognition, Neural Network

Abstract

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

  1. 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.
  2. 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.
  3. V.K. Govindan and A.P. Shivaprasad, “Character Recognition – A review,” Pattern Recognition, vol. 23, no. 7, pp. 671- 683, 1990
  4. 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.
  5. 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
  6. Abdulllah, M., Agal, A., Alharthi, M., & Alrashidi, M. (2018). Retracted: Arabic handwriting recognition using neural network
  7. Hertz, J. A. (2018). Introduction to the theory of neural computation. Boca Raton: CRC Press.
  8. Hamid, N. A., & Sjarif, N. N. A. (2017). Handwritten recognition using SVM, KNN and neural network. arXiv preprint arXiv:1702.00723.
  9. 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.
  10. Kurková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., & Maglogiannis, I. (2018).
  11. 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.
  12. 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.
  13. 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.

Downloads

Published

2021-08-30

Issue

Section

Research Articles

How to Cite

[1]
Monika, Monika Ingole, Khemutai Tighare, " Neural Network Based Handwritten Character Recognition " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.335-340, July-August-2021. Available at doi : https://doi.org/10.32628/CSEIT217472