Kathakali Hand Gesture Recognition With Minimal Data
Kavitha Raju Nandini J. Warrier
The Indian classical dance-drama Kathakali has a set of hand gestures called
Mudras, which form the fundamental units of all its dance moves and postures.
Recognizing the depicted mudra becomes one of the first steps in its digital
processing. The work treats the problem as a 24-class classification task and
proposes a vector-similarity-based approach using pose estimation, eliminating
the need for further training or fine-tuning. This approach overcomes the
challenge of data scarcity that limits the application of AI in similar
domains. The method attains 92% accuracy which is a similar or better
performance as other model-training-based works existing in the domain, with
the added advantage that the method can still work with data sizes as small as
1 or 5 samples with a slightly reduced performance. Working with images,
videos, and even real-time streams is possible. The system can work with
hand-cropped or full-body images alike. We have developed and made public a
dataset for the Kathakali Mudra Recognition as part of this work.