Preserving Cultural Heritage: Mapping of Handwritten Modi Script to Devanagari Characters

Preserving Cultural Heritage: Mapping of Handwritten Modi Script to Devanagari Characters

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-8
Year of Publication : 2024
Author : Haazique Sayyed, Vaibhavi Pathak, Pranav Wagholikar, Subhojit Talukdar, Nakul Sharma
DOI : 10.14445/22315381/IJETT-V72I8P107

How to Cite?

Haazique Sayyed, Vaibhavi Pathak, Pranav Wagholikar, Subhojit Talukdar, Nakul Sharma, "Preserving Cultural Heritage: Mapping of Handwritten Modi Script to Devanagari Characters," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 54-61, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P107

Abstract
The preservation of cultural heritage through the digitization of historical scripts stands as a testament to the fusion of technology and legacy. This research delves into the development of an automated system that reduces the gap between the ancient Modi script and contemporary digitalization, specifically the conversion from handwritten Modi script to Devanagari script. By leveraging advanced machine learning techniques, a character recognition model capable of interpreting diverse handwritten Modi script styles was engineered. Subsequently, a conversion algorithm was implemented to translate recognized Modi characters into the standardized Devanagari script accurately. The methodology involved meticulous data collection, training, and testing of the recognition model. Results showcase the system's efficacy in accurately recognizing and converting Modi script characters into their Devanagari counterparts across various handwriting styles and complexities. The importance of current work is its contribution to the preservation and accessibility of cultural artifacts, enabling the digitization of historical manuscripts and documents. This work not only offers a technological solution but also serves as a pathway for the conservation and revival of the rich cultural heritage embedded within the Modi script.

Keywords
Devanagari script, Handwritten character recognition, Modi Script, Optical character recognition.

References
[1] Solley Joseph, and Jossy P. George, “Offline Character Recognition of Handwritten MODI Script Using Wavelet Transform and Decision Tree Classifier,” Information and Communication Technology for Competitive Strategies, Springer, Singapore, vol. 191, pp. 509-517, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Solley Joseph, and Jossy P. George, “Recognition of Characters in Indian MODI Script,” Information Computing and Communication (ICGTSPICC), Jalgaon, India, pp. 236-240, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Savitri Laxmanrao Chandure, and Vandana Inamdar, “Performance Analysis of Handwritten Devnagari and MODI Character Recognition System,” International Conference on Computing, Analytics and Security Trends (CAST), Pune, India, pp. 513-516, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Omer Aydin, “Classification of Documents Extracted from Images with Optical Character Recognition Methods,” Anatolian Journal of Computer Sciences, vol. 6, no. 2, pp. 46-55, 2021.
[Google Scholar] [Publisher Link]
[5] Savitri Chandure, and Vandana Inamdar, “Handwritten MODI Character Recognition Using Transfer Learning with Discriminant Feature Analysis,” IETE Journal of Research, vol. 69, no. 5, pp. 2584-2594, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Juhee Sachdeva, and Sonu Mittal, “Handwritten Offline Devanagari Compound Character Recognition Using CNN,” Proceedings of Data Analytics and Management, Lecture Notes on Data Engineering and Communications Technologies, Singapore, vol. 90, pp. 211-220, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Solley Joseph, and Jossy George, “Handwritten Character Recognition of MODI Script using Convolutional Neural Network Based Feature Extraction Method and Support Vector Machine Classifier,” IEEE 5th International Conference on Signal and Image Processing (ICSIP), Nanjing, China, pp. 32-36, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Anam Sidra, and Gupta Saurabh, “An Approach for Recognizing Modi Lipi using Ostu’s Binarization Algorithm and Kohenen Neural Network,” International Journal of Computer Applications, vol. 111, no. 2, pp. 29-34, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Kirti Mahajan, and Niket Tajne, “An Ancient Indian Handwritten Script Character Recognition by Using Deep Learning Algorithm,” EFFLATOUNIA - Multidisciplinary Journal, vol. 5, no. 2, 2021.
[Google Scholar] [Publisher Link]
[10] Ritik Dixit, Rishika Kushwah, and Samay Pashine, “Handwritten Digit Recognition using Machine and Deep Learning Algorithms,” International Journal of Computer Applications, vol. 176, no. 42, pp. 27-33, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Pushkar Sohoni, “Marathi of a Single Type: The Demise of the Modi Script,” Modern Asian Studies, vol. 51, no. 3, pp. 662-685, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Modi alphabet, Omniglot. [Online]. Available: https://www.omniglot.com/writing/modi.htm
[13] Sadanand A. Kulkarni et al., “Offline Handwritten MODI Character Recognition Using HU, Zernike Moments and Zoning,” arXiv, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[14] S.N. Srihari, and E.J. Keubert, “Integration of Handwritten Address Interpretation Technology into the United States Postal Service Remote Computer Reader System,” Proceedings of the Fourth International Conference on Document Analysis and Recognition, Ulm, Germany, vol. 2, pp. 892-896, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Samuel Macêdo, Givânio Melo, and Judith Kelner, “A Comparative Study of Grayscale Conversion Techniques Applied to SIFT Descriptors,” Journal on Interactive Systems, vol. 6, no. 2, pp. 30-36, 2015.
[Google Scholar] [Publisher Link]
[16] Linwei Fan et al., “Brief Review of Image Denoising Techniques,” Visual Computing for Industry, Biomedicine, and Art, vol. 2, pp. 1-12, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Rafael C. Gonzalez, Digital Image Processing, Prentice Hall, pp. 1-954, 2008.
[Google Scholar] [Publisher Link]
[18] Khairun Saddami et al., “Improvement of Binarization Performance Using Local Otsu Thresholding,” International Journal of Electrical and Computer Engineering, vol. 9, no. 1, pp. 264-272, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Moseley Christopher, Atlas of the World's Languages in Danger, Memory of Peoples, UNESCO, pp. 1-218, 2010.
[Google Scholar] [Publisher Link]
[20] Charu C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Basingstoke, England: Springer, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shifei Ding et al., “Extreme Learning Machine: Algorithm, Theory and Applications,” Artificial Intelligence Review, vol. 44, pp. 103-115, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Abdelhak Boukharouba, and Abdelhak Bennia, “Novel Feature Extraction Technique for the Recognition of Handwritten Digits,” Applied Computing and Informatics, vol. 13, no. 1, pp. 19-26, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Li Chen et al., “Beyond Human Recognition: A CNN-Based Framework for Handwritten Character Recognition,” 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, pp. 695-699, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Michael Keeble Buckland, Emanuel Goldberg and His Knowledge Machine: Information, Invention, and Political Forces, Libraries Unlimited, pp. 1-380, 2006.
[Google Scholar] [Publisher Link]
[25] A.A. Chandio et al., “A Novel Approach for Online Sindhi Handwritten Word Recognition using Neural Network,” Sindh University Research Journal, vol. 48, no. 1, pp. 213-216, 2016.
[Google Scholar] [Publisher Link]
[26] Valentina Emilia Balas et al., Handbook of Deep Learning Applications, Springer International Publishing, pp. 1-383, 2019.
[Google Scholar] [Publisher Link]
[27] Jay H. Lee, Joohyun Shin, Matthew J. Realff et al., “Machine Learning: Overview of the Recent Progresses and Implications for the Process Systems Engineering Field,” Computers & Chemical Engineering, vol. 114, pp. 111-121, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Marco Gribaudo, and Mauro Iacono, Theory and Application of Multi-Formalism Modeling, Hershey, PA: IGI Global, pp. 1-314, 2013.
[Google Scholar] [Publisher Link]