Automatic multi-class classification of geographical indication tagged tribal art using deep learning

Abstract

In the field of computer vision, deep learning has significantly improved automated image classification, yet the classification of Indian tribal artworks remains largely unexplored. These art forms exhibit substantial stylistic overlap, variations in color palettes, and complex hand-drawn patterns, making manual identification difficult and automated classification even more challenging. Existing methods for artwork recognition are limited by small datasets, domain-specific variations, and a lack of curated tribal art collections. This study addresses these gaps by developing a deep learning based framework for the multi-class classification of three culturally significant Indian tribal arts: Manjusha, Gond, and Warli. A manually curated dataset of 900 images was augmented using geometric and color based transformations to increase robustness. The model is built on the Inception-ResNet-V2 architecture using transfer learning. Experimental evaluation demonstrates high performance, achieving 98.21% testing accuracy and stable validation trends. The proposed approach provides a scalable baseline for tribal art classification, supporting digital heritage preservation and future development of retrieval or educational systems.