  We propose a deep and interpretable probabilistic generative model to analyze
glyph shapes in printed Early Modern documents. We focus on clustering
extracted glyph images into underlying templates in the presence of multiple
confounding sources of variance. Our approach introduces a neural editor model
that first generates well-understood printing phenomena like spatial
perturbations from template parameters via interpertable latent variables, and
then modifies the result by generating a non-interpretable latent vector
responsible for inking variations, jitter, noise from the archiving process,
and other unforeseen phenomena associated with Early Modern printing.
Critically, by introducing an inference network whose input is restricted to
the visual residual between the observation and the interpretably-modified
template, we are able to control and isolate what the vector-valued latent
variable captures. We show that our approach outperforms rigid interpretable
clustering baselines (Ocular) and overly-flexible deep generative models (VAE)
alike on the task of completely unsupervised discovery of typefaces in
mixed-font documents.
