  Indian folk paintings have a rich mosaic of symbols, colors, textures, and
stories making them an invaluable repository of cultural legacy. The paper
presents a novel approach to classifying these paintings into distinct art
forms and tagging them with their unique salient features. A custom dataset
named FolkTalent, comprising 2279 digital images of paintings across 12
different forms, has been prepared using websites that are direct outlets of
Indian folk paintings. Tags covering a wide range of attributes like color,
theme, artistic style, and patterns are generated using GPT4, and verified by
an expert for each painting. Classification is performed employing the
RandomForest ensemble technique on fine-tuned Convolutional Neural Network
(CNN) models to classify Indian folk paintings, achieving an accuracy of
91.83%. Tagging is accomplished via the prominent fine-tuned CNN-based
backbones with a custom classifier attached to its top to perform multi-label
image classification. The generated tags offer a deeper insight into the
painting, enabling an enhanced search experience based on theme and visual
attributes. The proposed hybrid model sets a new benchmark in folk painting
classification and tagging, significantly contributing to cataloging India's
folk-art heritage.
