  The classical dances from India utilize a set of hand gestures known as
Mudras, serving as the foundational elements of its posture vocabulary.
Identifying these mudras represents a primary task in digitizing the dance
performances. With Kathakali, a dance-drama, as the focus, this work addresses
mudra recognition by framing it as a 24-class classification problem and
proposes a novel vector-similarity-based approach leveraging pose estimation
techniques. This method obviates the need for extensive training or
fine-tuning, thus mitigating the issue of limited data availability common in
similar AI applications. Achieving an accuracy rate of 92%, our approach
demonstrates comparable or superior performance to existing
model-training-based methodologies in this domain. Notably, it remains
effective even with small datasets comprising just 1 or 5 samples, albeit with
a slightly diminished performance. Furthermore, our system supports processing
images, videos, and real-time streams, accommodating both hand-cropped and
full-body images. As part of this research, we have curated and released a
publicly accessible Hasta Mudra dataset, which applies to multiple South Indian
art forms including Kathakali. The implementation of the proposed method is
also made available as a web application.
