A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing
Table 11: Results for character W
['[EMPTY]', '[BOLD] V-measure', '[BOLD] Mutual Info', '[BOLD] F&M', '[BOLD] NLL']
Early modern books were frequently composed from two or more type cases, resulting in documents with mixed fonts. We aim to learn the different shapes of metal stamps that were used as templates for each cluster component in our model. Data: In order to quantitatively evaluate our model’s performance, we experiment with synthetically generated realistic dataset for which we know the ground truth cluster identities in the following manner: For each character of interest, we pick three distinct images from scanned segmented EEBO images, corresponding to three different metal casts. Then we randomly add spatial perurbations related to scale, offset, rotation and shear. Finally, we add a small Gaussian noise to the pixel intensities and generate 300 perturbed examples per character class. Ocular and λ-only models fail because they lack expressiveness to explain the variations due to random jitters, erosions and dilations. The VAE-only model, while very expressive, performs poorly because it lacks the inductive bias needed for successful clustering. The No-residual model performs decently but our model’s superior performance emphasizes the importance of designing a restrictive inference network such that z doesn’t explain any variation due to the interpretable variables.