  In this work, we present an investigation into the use of neural feature
extraction in performing scribal hand analysis of the Linear B writing system.
While prior work has demonstrated the usefulness of strategies such as
phylogenetic systematics in tracing Linear B's history, these approaches have
relied on manually extracted features which can be very time consuming to
define by hand. Instead we propose learning features using a fully unsupervised
neural network that does not require any human annotation. Specifically our
model assigns each glyph written by the same scribal hand a shared vector
embedding to represent that author's stylistic patterns, and each glyph
representing the same syllabic sign a shared vector embedding to represent the
identifying shape of that character. Thus the properties of each image in our
dataset are represented as the combination of a scribe embedding and a sign
embedding. We train this model using both a reconstructive loss governed by a
decoder that seeks to reproduce glyphs from their corresponding embeddings, and
a discriminative loss which measures the model's ability to predict whether or
not an embedding corresponds to a given image. Among the key contributions of
this work we (1) present a new dataset of Linear B glyphs, annotated by scribal
hand and sign type, (2) propose a neural model for disentangling properties of
scribal hands from glyph shape, and (3) quantitatively evaluate the learned
embeddings on findplace prediction and similarity to manually extracted
features, showing improvements over simpler baseline methods.
