Image Processing-Based Recognition of Sattriya Dance Hand Mudras using a Hybrid CNN-SVM Model for Pattern Recognition in Signal Processing Applications

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 2

Year of Publication : 2026

Authors : Chayanika Sarmah, Parismita Sarma, Dankan Gowda V, Pooja Singh, Naveen B, Anil Kumar N

10.14445/23488549/IJECE-V13I2P105

How to Cite?

Chayanika Sarmah, Parismita Sarma, Dankan Gowda V, Pooja Singh, Naveen B, Anil Kumar N, "Image Processing-Based Recognition of Sattriya Dance Hand Mudras using a Hybrid CNN-SVM Model for Pattern Recognition in Signal Processing Applications," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 2, pp. 53-67, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I2P105

Abstract:

Classical dances like Sattriya dance have much cultural and artistic importance, and all the gestures carry a specific meaning or meanings in their forms of hand mudra. There is, however, an exception to this rule in terms of automatic recognition of these hand mudras owing to the different positions of the hands, lighting, and background. In this paper, a hybrid model of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) has been introduced for the recognition of a single-hand Sattriya dance mudras. The CNN model has the capability of inducting the high-level features in pictures of hand gestures and classifying them through the use of an SVM classifier. A complete dataset of 13 different hand mudras with variations added to their original forms to increase variations was used to train and test the model. Most classes showed high specificity with a precision score, recall score, and F1-score of the model of almost 1.0, meaning the power of the model to classify complicated mudra dance. The offered model shows how AI can be used to automate the identification of traditional dance movements, which can provide perspectives on digitization and preservation of the cultural art.

Keywords:

Sattriya dance, Hand mudras, Image Processing, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Gesture recognition, Deep Learning, Classification, Cultural preservation, AI.

References:

[1] Dimpee Baishya, “An Analytical Study of Dance Numbers of Sattriya Dance as practiced in Shri Shri Kamalabari Sattra,” Sangeet Galaxy, vol. 13, no. 2, pp. 171-177, 2024.
[Google Scholar] [Publisher Link]
[2] Pallavi Malavath, and Nagaraju Devarakonda, “Natya Shastra: Deep Learning for Automatic Classification of Hand Mudra in Indian Classical Dance Videos,” Artificial Intelligence Review, vol. 37, no. 3, pp. 45-56, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Poornachandra Sarang, Support Vector Machines: A Supervised Learning Algorithm for Classification and Regression, Thinking Data Science: A Data Science Practitioner’s Guide, pp. 153-165, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Laura Igual, and Santi Seguí, Supervised Learning: Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, pp. 67-97, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Debapratim Das Dawn, and Soharab Hossain Shaikh, “A Comprehensive Survey of Human Action Recognition with Spatio-Temporal Interest Point (STIP) Detector,” The Visual Computer, vol. 32, pp. 289-306, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Heng Wang, and Cordelia Schmid “Action Recognition with Improved Trajectories,” 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, pp. 3551-3558, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Saba Naaz, K.B. ShivaKumar, and B.D. Parameshachari, “Aggregation Signature of Multi Scale Features from Super Resolution Images for Bharathanatyam Mudra Classification for Augmented Reality Based Learning,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 3s, pp. 224-234, 2023.
[Google Scholar] [Publisher Link]
[8] Ahmad Jalal et al., “Robust Human Activity Recognition from Depth Video Using Spatiotemporal Multi Fused Features,” Pattern Recognition, vol. 61, pp. 295-308, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] M. Kalaimani, B. Latha, and A.N. Sigappi, “Survey on Hand Gesture Recognition in Bharathanatyam Mudras,” Telematique, vol. 21, no. 1, 2022.
[Publisher Link]
[10] Pravin R. Futane, and Rajiv V. Dharaskar, “Hasta Mudra: An Interpretation of Indian Sign Hand Gestures,” 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India, pp. 377-380, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Basavaraj S. Anami, and Venkatesh A. Bhandage, “A Comparative Study of Suitability of Certain Features in Classification of Bharathanatyam Mudra Images Using Artificial Neural Network,” Neural Processing Letters, vol. 50, pp. 741-769, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Konstantin Dergachov et al., “Data Pre-Processing to Increase the Quality of Optical Text Recognition Systems,” RadioElectronic and Computer Systems, vol. 4, pp. 183-198, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ahmad Naeem et al., “Deep Learned Vectors’ Formation using Auto-Correlation, Scaling, and Derivations with CNN for Complex and Huge Image Retrieval,” Complex & Intelligent Systems, vol. 9, pp. 1729-1751, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Passent El Kafrawy et al., “An Efficient SVM-based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data,” IEEE Access, vol. 9, pp. 155353-155369, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Heng Wang et al., “Action Recognition by Dense Trajectories,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’11), Colorado Springs, CO, USA, pp. 3169-3176, 2011.
[CrossRef] [Publisher Link]
[16] P.V.V. Kishore et al., “Optical Flow Hand Tracking and Active Contour Hand Shape Features for Continuous Sign Language Recognition with Artificial Neural Networks,” 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India, pp. 346-351, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Shuiwang Ji et al., “3D Convolutional Neural Networks for Human Action Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 221-231, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Aparna Mohanty et al., “Nrityabodha: Towards Understanding Indian Classical Dance Using a Deep Learning Approach,” Signal Processing: Image Communication, vol. 47, pp. 529-548, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Divya Hariharan, Tinku Acharya, and Sushmita Mitra, “Recognizing Hand Gestures of a Dancer,” 4th International conference on Pattern Recognition and Machine Intelligence, Moscow, Russia Springer, pp. 186-192, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[20] K.V.V. Kumar, and P.V.V. Kishore, “Indian Classical Dance Mudra Classification Using HOG Features and SVM Classifier,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2537-2546, 2017.
[CrossRef] [Google Scholar] [Publisher Link]