Handwritten Character Recognition using Neural Network and Tensor Flow
Megha Agarwal1, Shalika2, Vinam Tomar3, Priyanka Gupta4
1Megha Agarwal, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ghaziabad, India.
2Shalika, Department of Ministry Corporate Affairs, Krishna Institute of Engineering and Technology Group of Institutions Ghaziabd, India.
3Vinam Tomar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ghaziabad, India.
4Priyanka Gupta, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ghaziabad, India.
Manuscript received on 15 April 2019 | Revised Manuscript received on 22 April 2019 | Manuscript Published on 26 July 2019 | PP: 1445-1448 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12940486S419/19©BEIESP | DOI: 10.35940/ijitee.F1294.0486S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper will describe the best approach to get more than 90% accuracy in the field of Handwritten Character Recognition (HCR). There have been plenty of research done in the field of HCR but still it is an open problem as we are still lacking in getting the best accuracy. In this paper, the offline handwritten character recognition will be done using Convolutional neural network and Tensorflow. A method called Soft Max Regression is used for assigning the probabilities to handwritten characters being one of the several characters as it gives the values between 0 and 1 summing up to 1. The purpose is to develop the software with a very high accuracy rate and with minimal time and space complexity and also optimal.
Keywords: Handwritten Character Recognition, Convolutional Neural Network, Tensor Flow, Soft Max Regression.
Scope of the Article: Computer Science and Its Applications