Interpretable visual feature discovery using multiple instance learning: A case study of colonial Korean print

Computational Humanities Research, vol. 2

Abstract

Abstract Colonial Korean printshops (1910–1945) operated within severe material constraints that produced apparent typographic standardization. Manual bibliography has nevertheless identified subtle shop-specific signatures in letterforms. This article tests whether computational methods can detect these signatures systematically across large corpora. We apply multiple instance learning to 57,583 page images from four major printshops in Kyŏngsŏng (Seoul). To avoid same-book leakage, we evaluate page classification with all pages from each tested book held out from training. Under this stricter protocol, the model assigns pages to printshops with 84.5 percent accuracy while producing interpretable attention maps. Clustering high-attention patches reveals that the four presses occupy the same regions of the embedding space, reflecting their shared typographic infrastructure. Differentiation emerges through density patterns within this shared space rather than through distinct typographic vocabularies. Each press concentrates disproportionately in clusters capturing specific sub-character features, such as terminal angles, serif weight, and stroke thickness. These computationally identified patterns align with features that De Fremery and Ryu documented through intensive manual analysis of individual volumes. The correspondence supports the approach while extending it to scales that manual bibliography cannot achieve. This framework shows how computational analysis can test and extend humanities scholarship on historical print culture by surfacing the specific visual features that distinguish institutional outputs within materially constrained production systems.

Authors 3

1. Aron Van de Pol Aachen

   Leiden University

   Affiliation as printed

   Leiden University

2. Jelena Prokić Aachen

   Leiden University

   Affiliation as printed

   Leiden University

3. Angus Mol Aachen

   Leiden University

   Affiliation as printed

   Leiden University