Exploring ResNet101, InceptionV3, and Xception for Modi Script Character Classification

Authors

  • Ravindra Sonavane Dept. of Artificial Intelligence & Data Science, Thakur College of Engineering & Technology, Mumbai, India
  • Pandharinath Ghonge Dept. of Electronics and Telecommunication Engineering, SJCEM, Palghar, Mumbai, India
  • Sandip Umajirao Patil Dept.of Electronics and Computer Science Department, St. John College of Engineering and Management, Palghar, Mumbai, India
  • Kaustubh Shivaji Sagale Dept. Electronics and Telecommunication Engineering, R.C. Patel Institute of Technology, Shirpur, Dhule, India
  • Anand Arvind Maha Dept. of Artificial Intelligence and Machine Learning, TCET, Kandivali, Mumbai, India

Keywords:

Character recognition, Modi script, segmentation, recognition, classification

Abstract

The "MODI lipi" script, which was used historically in Maharashtra, Western India, to record religious writings, and which was the official script used by the Maratha administration from the 17th century until the mid-1900s, has a rich cultural legacy. Even though the "Manuscript" is a valuable source of inspiration and knowledge from a bygone age, modern audiences are not as familiar with it. Recognizing its potential to inspire and educate the present generation, there is a need to develop a sophisticated recognition system for MODI within handwritten character recognition. Deep learning-based algorithms, such as ResNet101, InceptionV3, and Xception, have demonstrated remarkable efficacy in various pattern identification applications, including character recognition. In the current landscape, transfer learning algorithms, especially those leveraging ResNet101, InceptionV3, and Xception architectures, have gained prominence for significantly enhancing recognition task outcomes. This study specifically proposes implementing these advanced deep-learning models to classify MODI characters. By harnessing the power of ResNet101, InceptionV3, and Xception algorithms, the aim is to optimize the recognition accuracy and efficiency of the MODI script, making it more accessible and comprehensible for today's youth. This research endeavors to unlock the untapped potential of MODI script as a valuable cultural and educational resource by utilizing state-of-the-art deep learning methodologies.

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References

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Published

23.02.2024

How to Cite

Sonavane, R. ., Ghonge, P. ., Patil, S. U. ., Sagale, K. S. ., & Maha, A. A. . (2024). Exploring ResNet101, InceptionV3, and Xception for Modi Script Character Classification . International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 117–124. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4841

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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