DEEP LEARNING-BASED BHARATANATYAM MUDRA RECOGNITION USING LSTM AND GRU ARCHITECTURES.

Deep learning techniques, particularly Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, offer effective solutions for recognizing Bharatanatyam mudras. These intricate gestures are essential for conveying the rich narratives and emotions inherent in this classical dance form. By training models on extensive datasets of labeled Bharatanatyam movements, the system can accurately detect subtle variations in gestures and expressions, significantly improving mudra recognition. This capability not only deepens practitioners' understanding of Bharatanatyam but also provides a valuable tool for storytelling during performances. Furthermore, it equips dance instructors with an effective method to demonstrate mudras to students, fostering a more engaging and interactive learning environment. Future extensions of this work could involve developing a system capable of real-time correction and verification of mudra positions, offering immediate feedback through display interfaces powered by deep neural networks. Overall, this research contributes to the preservation of Bharatanatyam as a digital heritage, ensuring its cultural significance endures in the modern era while enhancing both its educational and artistic value. [ABSTRACT FROM AUTHOR]