FOLK ART RECOGNITION USING DEEP LEARNING ALGORITHMS - Patil, Shashikant; Rawat, Kalpana; Pimple, Jagdish; Subudhi, Asit Kumar; Bareja, Lakshay; Vashisht, Nitish (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The given work offers a deep learning-driven architecture of identifying Indian folk art traditions with the help of a hybrid framework that integrates convolutional networks and Vision Transformers and CLIP-driven semantic alignment. A curated and annotated multi-source dataset was prepared based on a culturally informed tradition, region and sub style taxonomy. The issues of visual diversity and imbalance of the classes were resolved with rigorous preprocessing, motif-preserving augmentations, and training based on classes. It was found that the hybrid architecture performed well both with classification and retrieval tasks, and showed strong macro-F1 scores and clearly separated embedding clusters of the major folk traditions. Confusion analysis showed styles that were visually overlapping and embedding-space visualizations ensured that the model was capable of capturing significant cultural differences. These findings suggest that deep learning can be effectively used in complex artistic areas that encourage scalable cultural documentation, digital archiving, and heritage education. The suggested framework forms a powerful foundation on upcoming improvements of generative augmentation, cross-domain adaptation and interactive cultural exploration tools.