Handwritten Character Recognition Using Neural Network

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/CSEIT217460

Keywords:

Handwritten Recognition, Neural Network.

Abstract

In this paper, an enterprise is made to perceive manually written characters for English letters so as. The precept point of this mission is to plan a master framework for, "HCR(English) utilizing neural community". That could viably understand a particular individual-of-kind layout making use of the artificial neural community approach. The manually written man or woman acknowledgment trouble has grown to be the maximum famous trouble in ai. Handwritten man or woman acknowledgment has been a difficult space of exam, with the execution of gadgets getting to know we suggest a neural network-based methodology. The development is based totally on neural network, that is a subject of look at in artificial intelligence. Distinct strategies and methods are used to broaden a handwriting person recognition system. Acknowledgment, precision fee, execution, and execution time are massive versions on the way to be met through the technique being applied. The purpose is to illustrate the effectiveness of neural networks for handwriting character recognition.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-08-30

Issue

Volume 7, Issue 4, July-August-2021

Section

Research Articles

License

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Monika, Monika Ingole, Khemutai Tighare, " Handwritten Character Recognition Using Neural Network" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 4, pp.203-207, July-August-2021. Available at doi : https://doi.org/10.32628/CSEIT217460