CNN-POWERED MARATHI SIGN DIALECT ACKNOWLEDGMENT AND TRANSLATION

Authors

  • Prof. Snehal Mangale Department of Computer Engineering, Dr. D. Y. Patil College of Engineering & Innovation, Talegaon, India
  • Raj Kashid Department of Computer Engineering, Dr. D. Y. Patil College of Engineering & Innovation, Talegaon, India
  • Aditya Kamble Department of Computer Engineering, Dr. D. Y. Patil College of Engineering & Innovation, Talegaon, India
  • Vaishnavi Jangam Department of Computer Engineering, Dr. D. Y. Patil College of Engineering & Innovation, Talegaon, India
  • Mohan Kharat Department of Computer Engineering, Dr. D. Y. Patil College of Engineering & Innovation, Talegaon, India

Keywords:

Marathi Sign Dialect, Convolutional Neural Networks, Gesture Recognition, Data Scarcity, Hybrid Approaches

Abstract

This paper presents a comprehensive review of recent advancements in Marathi Sign Dialect (MSL) recognition and interpretation, focusing on the application of Convolutional Neural Networks (CNNs). Existing research demonstrates the effectiveness of CNNs in extracting and classifying features from MSL gestures, leading to promising accuracy rates. Despite these advancements, challenges such as data scarcity and the inherent complexity of MSL remain. To address these limitations, future research should explore hybrid approaches that integrate CNNs with Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) systems. By combining the strengths of these techniques, we can anticipate further improvements in MSL recognition and interpretation, ultimately enhancing communication accessibility for the Marathi sign language community

References

I. S. Dahibavkar, J. Dhopte, M. Patole, and S. Madachane, “Marathi Sign Language Recognition,” International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 9, no.5, pp. 1-5, 2020.

II. A. Shinde and R. Kagalkar, “Advanced Marathi Sign Language Recognition using Computer Vision,” International Journal of Computer Applications, vol. 118, no. 13, pp. 1-5, 2015.

III. P. Dahatonde, P. Khandekar, O. Kharat, S. Saoji, and N. Jayakumar, “Sign Language Recognition and Translation to English and Marathi,” International Journal of Creative Research Thoughts (IJCRT), vol. 12, no. 5, p. 481, 2024.

IV. S. Patil, V. M. Lomte, Y. Pawar, S. Raysing, and A. Renuse, “Survey on Sign Language Detection Using Smart Gloves for Disabled People,” International Journal of Creative Research Thoughts (IJCRT), vol. 10, no. 6, p. 276, 2023.

V. Y. Gajare, V. M. Lomte, H. Bhujbal, A. Gurav, and I. Gulanagoudar, “Hand Gesture Recognition System for Translating Sign Language into Text and Speech,” International Journal of Creative Research Thoughts (IJCRT), vol. 10, no. 6, p. 276, 2022.

VI. V. M. Lomte and D. D. Doye, “Gesture-Based Devanagari Text Recognition Using Deep Learning,” Journal of Harbin Institute of Technology, vol. 54, no. 6, p. 238, 2022.

VII. P. Rokade, N. Sali, D. Shinde, and S. Yadav, “Indian Sign Language Recognition System in Marathi Language Text,” International Journal of Computer Sciences and Engineering, vol. 7, no. 5, [Open Access Research Paper], E-ISSN: 2347-2693, 2019.

VIII. H. R. Khandagale, R. R. Chougale, and R. R. Jadhav, “Real Time Sign Language Recognition Based on Machine Learning Survey,” International Research Journal of Modernization in Engineering Technology and Science, vol. 5, no. 4, [PeerReviewed, Open Access], E-ISSN: 2582-5208, 2023.

IX. A. Sanap, V. M. Lomte, A. Joshi, A. Nasare, and S. Chandratre, “Handwritten Character Recognition from Physical Documentation,” International Journal of Research and Analytical Reviews, vol. 10, no. 2, [Open Access Research Paper], EISSN: 2348-1269, P-ISSN: 2349-5138, 2023.

X. S. Patil, V. M. Lomte, Y. Pawar, S. Raysing, and A. Renuse, “ECOSH: A Novel Technique For Sign Language Detection Using IoT Based Smart Hand Gloves,” Journal of Technology, vol. 12, no. 5, ISSN: 10123407, 2024.

XI. A. A. Darekar, N. B. Pawar, R. D. Pawar, V. S. Sangale, and Prof. M. V. Khandekar, “Marathi Sign Language Recognition for Physically Disabled People,” International Journal of Advanced Research in Science, Communication and Technology, vol. 2, no. 7, [Open Access Research Paper], E-ISSN: 2581-9429, DOI: 10.48175/IJARSCT-4435, 2022.

Additional Files

Published

15-10-2024

How to Cite

Prof. Snehal Mangale, Raj Kashid, Aditya Kamble, Vaishnavi Jangam, & Mohan Kharat. (2024). CNN-POWERED MARATHI SIGN DIALECT ACKNOWLEDGMENT AND TRANSLATION. International Education and Research Journal (IERJ), 10(10). Retrieved from https://ierj.in/journal/index.php/ierj/article/view/3711