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Evaluating Hierarchy-Aware Deep Learning for the Recognition of Tironian Notes

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The paper evaluates whether incorporating the hierarchical structure of Tironian notes can improve automatic recognition of this complex Latin shorthand system. Experiments compare flat classifiers (ResNet18, ConvNeXt, Swin, ViT) with hierarchy‑aware models (HD‑CNN and routing approaches) on handwritten and manuscript samples, with and without few‑shot adaptation. Results show that hierarchical models outperform flat ones when no adaptation is applied, but flat models surpass them after few‑shot adaptation, indicating that hierarchy can aid recognition under non‑adapted conditions.

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