Papers by Christopher Warren
A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing (2020.acl-main)
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| Challenge: | Scholars often need to go beyond textual analysis for establishing provenance of historical documents. |
| Approach: | They propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents by generating a latent vector responsible for inking variations, jitter, noise and other unforeseen phenomena. |
| Outcome: | The proposed model outperforms interpretable clustering baselines and overly-flexible deep generative models on the task of completely unsupervised discovery of typefaces in mixed-fonts documents. |