DIGITAL ARCHIVING OF FOLK ART THROUGH MACHINE LEARNING
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7083Keywords:
Digital Archiving, Folk Art Preservation, Machine Learning, Computer Vision, Hybrid CNN–Transformer Models, Contrastive Learning, Class Imbalance, Content-Based Image RetrievalAbstract [English]
Computational problems in the digital preservation of folk art are distinct because of the visual diversities, long tail distributions of motifs, and incomplete meta-data. The proposed paper presents a machine learning-based intelligent system of digital archiving folk art, which incorporates the visual feature learning, the automatic mechanisms of semantic enrichment, and the scaling of the retrieval mechanisms. The method uses both a hybrid convolutional neural network and vision transformer architecture to learn local motifs patterns as well as global compositional patterns. In order to tackle the class imbalance and style difference, contrastive self-supervised learning is used together with imbalance-aware supervised losses, such as class reweighting and focal loss. The expert assisted and weakly supervised annotations are used to form a structured dataset and then it is rigorously statistically analyzed to inform the model design. The experimental outcomes indicate that there is a high enhancement in the macro-averaging classification performance and content-based image retrieval precision, especially in the underrepresented motifs. System surface analysis indicates that embedding-based indexing and use of approximate nearest-neighbor search has a low query latency and strong retrieval fairness of motif frequencies. The suggested framework can allow scalable, equitable, and semantically meaningful digital archiving which serves as a generalizable answer to cultural heritage preservation and multimedia archival systems.
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Copyright (c) 2026 Nagajayant Nagamani, Kalpana Rawat, Prashant Anerao, Dr. Rajesh Uttam Kanthe, Atish Baburao Mane, Ponmurugan Panneerselvam
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