What does this research mean for the field?

An intelligent visual-symbol extraction framework integrating convolutional neural networks and generative adversarial networks achieves 92.7% extraction accuracy and reduces processing time to 0.8 seconds per sample for digitizing intangible cultural heritage. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a deep feature fusion framework that efficiently extracts visual symbols from intangible cultural heritage while preserving morphological details and cultural semantics.

August 16, 2026Advanced ElectromagneticsOpen Access

Research on Intelligent Extraction of Visual Symbols and Creative Transformation Methods in the Digital Inheritance of Intangible Cultural Heritage

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Authors

SYS. H. Yue

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Implication

Experimental framework demonstrates 92.7% extraction accuracy across cultural heritage visual symbols, highlighting an efficient approach for digital preservation and creative redesign.

Key Points

To develop a deep feature fusion framework that efficiently extracts visual symbols from intangible cultural heritage while preserving morphological details and cultural semantics.
Analyzed representative samples of paper-cutting, shadow puppetry, and batik across morphology, color, texture, and cultural semantic dimensions to construct a feature database.
Integrated convolutional neural networks and generative adversarial networks with multi-window deconvolution decoupling, overlapping masks, delay compensation, and topological consistency constraints.
Introduced a Cultural Preservation Index to evaluate symbol retention and tested multidimensional creative transformation strategies across 800 visual-symbol samples.
Achieved an extraction accuracy of 92.7% across 800 visual-symbol samples, outperforming single-CNN baseline extraction models.
Reduced average processing time to 0.8 seconds per sample while achieving an 86.2% user recognition rate on transformed creative works.

Cite This Study

S. H. Yue (2026) studied this question.

synapsesocial.com/papers/6a817a72f2fb91fc834ae429 https://doi.org/10.7716/aem.v15i3.4024

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Implication

Experimental framework demonstrates 92.7% extraction accuracy across cultural heritage visual symbols, highlighting an efficient approach for digital preservation and creative redesign.

Research on Intelligent Extraction of Visual Symbols and Creative Transformation Methods in the Digital Inheritance of Intangible Cultural Heritage

Discussion

Member takes

Implication

Key Points

Cite This Study

Research on Intelligent Extraction of Visual Symbols and Creative Transformation Methods in the Digital Inheritance of Intangible Cultural Heritage

Discussion

Member takes

Implication

Key Points

Cite This Study

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