COMPUTATIONAL PATTERN RECOGNITION FOR IDENTIFYING CULTURAL SYMBOLISM IN REGIONAL ART FORMS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7478Keywords:
Computational Pattern Recognition, Cultural Symbolism, Regional Art, Deep Learning, Convolutional Neural Networks, Feature Extraction, Multimodal Learning, Cultural Heritage Preservation, Explainable AiAbstract [English]
Regional art forms have a rich cultural symbolism that defines the beliefs, identities and traditions of various communities. Nevertheless, these symbolic elements are usually interpreted subjectively, in an intensive and expensive way, and restricted by the number of domain experts. In this paper, a proposal has been made on a computational design to recognize the cultural symbolism of regional art through advanced pattern recognition and recognition algorithms. The strategy incorporates the elements of image processing, feature extraction, deep learning, and cultural knowledge modeling to allow the analysis of artistic patterns that are automated and context-dependent. The framework uses preprocessing and segmentation in isolating valuable visual attributes and then uses feature extraction techniques to capture color, texture and shape attributes. A hybrid deep learning model based on Convolutional Neural Networks (CNNs) and attention is applied to acquire both the local and global representations of symbolic patterns. Additionally, a cultural knowledge base is also added to annotate the patterns that are identified to their semantic meanings to help to read between the lines better. The experimental outcomes prove that the suggested model performs on a high level and the level of accuracy reaches over 90 percent, the stabilities of precision and recall between different symbol categories are relatively high, as well. The comparative analysis with the baseline models shows the excellence of the proposed method in identification of multifarious and multifaceted symbolic elements. Attention visualization as the form of the explainable outputs of the system makes it applicable to the cultural heritage preservation, digital museums, and to the AI-aided interpretation of art.
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Copyright (c) 2026 Jyotsna Suryavanshi, Nivetha N, Dr. Kajal Thakuriya, Dr. Irphan Ali, Dikshit Sharma, Mr. Mahendihasan S. Heera, Muninathan N
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