GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
The spatial view (the grid of rectangles) and the tree view (the hierarchy of nodes) represent the same structure. Searching for a point means walking down the tree: at each node, you check which of the four children contains your target coordinate and recurse into that child. You never visit the other three.
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Трамп высказался о непростом решении по Ирану09:14
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