HaSPeR: An Image Repository for Hand Shadow Puppet Recognition
Syed Rifat Raiyan, Zibran Zarif Amio, Sabbir Ahmed

TL;DR
This paper introduces HaSPeR, a comprehensive dataset and baseline analysis for hand shadow puppet recognition, aiming to preserve and promote this traditional art form using computer vision techniques.
Contribution
It provides the first large-scale dataset and baseline models for hand shadow puppet recognition, including detailed analysis and a prototype application for art preservation.
Findings
Skip-connected convolutional models outperform transformer architectures.
Lightweight models like MobileNetV2 perform well for mobile applications.
ResNet34 offers the best performance and interpretability insights.
Abstract
Hand shadow puppetry, also known as shadowgraphy or ombromanie, is a form of theatrical art and storytelling where hand shadows are projected onto flat surfaces to create illusions of living creatures. The skilled performers create these silhouettes by hand positioning, finger movements, and dexterous gestures to resemble shadows of animals and objects. Due to the lack of practitioners and a seismic shift in people's entertainment standards, this art form is on the verge of extinction. To facilitate its preservation and proliferate it to a wider audience, we introduce , a novel dataset consisting of 15,000 images of hand shadow puppets across 15 classes extracted from both professional and amateur hand shadow puppeteer clips. We provide a detailed statistical analysis of the dataset and employ a range of pretrained image classification models to establish…
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Taxonomy
TopicsHand Gesture Recognition Systems · Face recognition and analysis · Medical Imaging and Analysis
MethodsPointwise Convolution · Depthwise Convolution · Depthwise Separable Convolution · Batch Normalization · Inverted Residual Block · 1x1 Convolution · Convolution · Average Pooling
