Machine Learning-Based Classical Dance Mudra Recognition Model

Title

Machine Learning-Based Classical Dance Mudra Recognition Model

Creator

Manoj, Manjeera; Yamini, Kotapati; Pranathi, Dampetla Sai; Jayapandian, N.

Description

In this research, symbolic hand mudras of the Indian traditional dance style of Bharatanatyam are recognized and categorized using deep learning techniques. The three main goals are establishing baseline datasets to identify and categorize hasta mudras, designing an automated tutoring program for prospective students, and constructing a system for recommending videos that support cultural heritage. The research achieves a real-time recognition accuracy of 85% to 95% using convolutional neural networks (CNNs) and the Mobile Net architecture. This activity greatly aids virtual learning during pandemics, worldwide cultural relations, and preserving intangible cultural assets. The three main goals of this research are to establish baseline datasets for accurate mudra identification, create an automated tutoring program for participants, and build a video recommendation system to promote cultural heritage globally. The benchmark datasets that are used to train the models are made up of high-quality photos and videos of mudras that are taken and annotated under the direction of experts. While the video recommendation system supports attempts to preserve culture and advance education, the automated tutoring system provides participants with a comprehensive virtual learning environment and tailored feedback. To ensure the survival and continued appreciation of Bharatanatyam around the world, our endeavor substantially enhances virtual education, deep learning, and cultural preservation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1278;pp.93-104

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial neural network; Bharatanatyam; Convolutional neural networks; Hidden Markov models; Traditional dance

Coverage

Manoj M., Department of Computer Science and Engineering, Christ (Deemed to be University), Kengeri Campus, Bangalore, India; Yamini K., Department of Computer Science and Engineering, Christ (Deemed to be University), Kengeri Campus, Bangalore, India; Pranathi D.S., Department of Computer Science and Engineering, Christ (Deemed to be University), Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ (Deemed to be University), Kengeri Campus, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981962702-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-2703-5_8

https://www.scopus.com/pages/publications/105006934242?origin=resultslist