Recognition of Handwritten Characters using Deep Convolutional Neural Network
Jagan Mohan Reddy D1, A Vishnuvardhan Reddy2
1Jagan Mohan Reddy D, Department of Computer Science & Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India.
2Dr. M Siva Koti Reddy, Department of Computer Science & Engineering G Pulla Reddy College of Engineering Kurnool, Andhra Pradesh, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 315-317 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10640486S419/19©BEIESP | DOI: 10.35940/ijitee.F1064.0486S419
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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: Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.
Keywords: The Proposed Work Can be Enhance the Recognition.
Scope of the Article: Computer Science