Single-Hand Gesture Recognition of Manipuri Classical Dance of India using a Supervised Machine Learning Approach

Hand gesture recognition plays a pivotal role in human-computer interaction and cultural heritage preservation. This paper presents an innovative machine learning approach for recognizing single-hand gestures in Manipuri classical dance, a prestigious Indian classical dance form. Manipuri dancers utilize 25 distinct singlehand gestures (Asamyukta mudra) to convey meaning and emotions. Leveraging a Support Vector Machine (SVM) classifier, we develop a dataset comprising 750 images to recognize these gestures, achieving an impressive test accuracy of 80.76%. Our contributions include the creation of a dedicated dataset for Manipuri classical dance single-hand gestures and the proposal of an SVM-based recognition method. This work not only advances the field of hand gesture recognition but also highlights the potential of machine learning in preserving and promoting cultural heritage