Hand Writing recognition System using Neural Networks
M.Sujatha1, V.Sandeep2, Ch. Aishwarya3, B.Mounika4
1Dr. M.Sujatha, Associate Professor, Jyothismathi Institute of Technology and Science, Ramakrishna Colony, Telangana
2V.Sandeep, B.Tech Students Jyothismathi Institute of Technology and Science, Ramakrishna Colony, Telangana
3Ch. Aishwarya, B.Tech Students Jyothismathi Institute of Technology and Science, Ramakrishna Colony, Telangana
5B.Mounika, B.Tech Students Jyothismathi Institute of Technology and Science, Ramakrishna Colony, Telangana
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4977-4980 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5310119119/2019©BEIESP | DOI: 10.35940/ijitee.A5310.119119
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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: Handwritten Recognition is a process of pattern recognition which defines ability of a system to identify characters. There are many applications of Handwritten recognition (HWR) system such as reading postal addresses, bank check amounts, mail sorting and many more. HWR systems transcribes human written text into digital text. Plenty of research done in the field of recognizing handwritten characters but lacking in best accuracy is a challenge. In this proposed technique, offline HWR is done using Neural networks(NN) and Tensorflow is proposed. The proposed technique used to build a system which will be able to recognize the hand written characters with highest accuracy. The experiment is performed on proposed technique with accuracy of 85.5% compared to the state-of-the-art.
Keywords: Hand Written Recognition, Neural networks, Tensor flow.
Scope of the Article: Artificial Intelligence