A Survey on Digitization of Handwritten Notes in Kannada
K Amulya1, Lakshmi Reddy2, M Chandara Kumar3, Rachana D4
1K Amulya, Department of Computer Technology, Dayananda Sagar University, Kudlu Gate Bangalore (Karnataka), India.
2Lakshmi Reddy, Department of Computer Technology, Dayananda Sagar University, Kudlu Gate Bangalore (Karnataka), India.
3M Chandara Kumar, Department of Computer Technology, Dayananda Sagar University, Kudlu Gate Bangalore (Karnataka), India.
4Rachana D, Department of Computer Technology, Dayananda Sagar University, Kudlu Gate Bangalore (Karnataka), India.
Manuscript received on 07 November 2022 | Revised Manuscript received on 17 November 2022 | Manuscript Accepted on 15 December 2022 | Manuscript published on 30 December 2022 | PP: 6-11 | Volume-12 Issue-1, December 2022 | Retrieval Number: 100.1/ijitee.A93501212122 | DOI: 10.35940/ijitee.A9350.1212122
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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: Recognition of handwritten text is still an unresolved research problem in the field of optical character recognition. This article suggests an efficient method for creating handwritten text recognition systems. This is a challenging subject that has received a lot of attention recently. A discipline known as optical character recognition makes it possible to convert many kinds of texts or photos into editable, searchable, and analyzable data. Researchers have been using artificial intelligence and machine learning methods to automatically evaluate printed and handwritten documents during the past ten years in order to digitize them. This review paper’s goals are to present research directions and a summary of previous studies on character recognition in handwritten texts. Since different people have different handwriting styles, handwritten characters might be challenging to read. Our “Digitization of handwritten notes” research and effort is to categorize and identify characters in the south Indian language of Kannada. The characters are extracted from printed texts and pre-processed using NumPy and OpenCV before being fed through a CNN.
Keywords: C-NN (Convoluted Neural Networks), Image Processing, Segmentation
Scope of the Article: Convoluted Neural Networks