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.

印度古典舞蹈剧 Kathakali 有一组手势称为 Mudras，它们构成了所有舞姿和动作的基本单元。本研究将这个问题视作一个 24 类分类任务，并提出了一种基于姿势估计的向量相似性方法，消除了进一步训练或微调的需要。这种方法克服了数据稀缺性的挑战，从而限制了在类似领域中应用人工智能的范围。该方法在艺术形式中达到了 92% 的准确率，与领域中现有的基于模型训练的作品相当或更好，而且该方法即使在只有 1 或 5 个样本的情况下仍可正常工作，性能略有降低。可用于图像、视频甚至实时流。该系统可以处理手部剪裁或全身图像。我们在这项工作中开发并公开了 Kathakali Mudra Recognition 数据集。