Robotic Handwritten Kannada Character Recognition using Neural Network
Shakunthala B. S1, C S Pillai2
1Dr. J. Katyayani, B.Tech., MBA., Ph.D.,M.Tech. Professor, Department of Business Management Sri Padmavathi Mahila Viswavidyalayam, Tirupathi-AP.
2Ch.Varalakshmi Research Scholar, Department of Business Management Sri Padmavathi Mahila Viswavidyalayam-Tirupathi-AP.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2498-2502 | Volume-8 Issue-10, August 2019 | Retrieval Number: J95490881019/2019©BEIESP | DOI: 10.35940/ijitee.J9549.0881019
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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: Data preparing and the board is basic now a days. In this paper, programmed preparing of structures written in Kannada language is considered. A reasonable pre-preparing procedure is introduced for separating written by hand characters. Essential Component Analysis (PCA) and Histogram of arranged Gradients (HoG) are utilized for highlight extraction. These highlights are sustained to multilayer feed forward back spread neural system for arrangement. Just 57 characters are utilized for acknowledgment. Exhibitions of two highlights are looked at for changed number of classes. Hoard is found to have preferred acknowledgment exactness over PCA as number of classes expanded. This is actualized in Visual Studio 2010 utilizing Open CV library
Keywords: Back Propagation Neural Network, Form Processing, Histogram of Gradients, Kannada Script, Principal Component Analysis.
Scope of the Article: Neural Network