Implementing a Hybrid Deep Learning Approach to Achieve Classic Handwritten Alphanumeric MODI Recognition
Maitreyi Ekbote1, Aishwary Jadhav2, Dayanand Ambawade3
1Maitreyi Ekbote, Student, Department of Electronics and Telecommunication, S.P.I.T., Mumbai (Maharashtra), India.
2Aishwary Jadhav, Student, Department of Electronics and Telecommunication, S.P.I.T., Mumbai (Maharashtra), India.
3Dayanand Ambawade, Associate Professor, Department of Electronics and Telecommunication, S.P.I.T., Mumbai (Maharashtra), India.
Manuscript received on 26 September 2022 | Revised Manuscript received on 29 September 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 58-62 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38461012122 | DOI: 10.35940/ijeat.A3846.1012122
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: MODI, synonymous with the Devanagari script, is an ancient script from the 17th century used by the Maratha empire as a symbol of culture and power to propagate Marathi. Due to a decline in its usage, absence of quality script database and an unavailability of good literature, identification and translation of MODI script is demanding. The present work deals with a novel study on the recognition of MODI characters and numerals by using Convolutional Neural Network (CNN) architecture. By using a traditional machine learning classifier, classification is performed, and then through a comparative analysis of Random Forest and XGBoost, the study achieves recognition accuracy of 92% for characters and 93.3% for numerals.
Keywords: CNN, Handwritten Character Recognition, MODI Script, Random Forest
Scope of the Article: Deep Learning