Handwriting Recognition using Deep Learning based Convolutional Neural Network
Asha K1, Krishnappa H K2
1Asha K*, Department of IS&E, GMIT, Davanagere, India.
2Krishnappa H K, Department of Computer Science Engineering, RVCE, Bengaluru, India.

Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4826-4828 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7811118419/2019©BEIESP | DOI: 10.35940/ijrte.D7811.118419

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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: Handwriting is a learned skill that had been an excellent means of communication and documentation for thousands of years. The simple way to communicate with the computers is through either speech or handwriting. Speech has some limitation; hence input through handwriting is recommended. It is difficult to input data for computers for Indian language scripts because of their com-plex character set. This paper focuses on exploring convolutional neural networks (CNN) which is deep learning based for the recognition of handwritten script. The proposed method has shown 99% for handwritten English numerals and promising recognition accuracy for Kannada numerals.
Keywords: CNN, Deep Learning, Handwriting recognition, Kannada OCR, Preprocessing, Segmentation.
Scope of the Article: Deep Learning.