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

Neural Representation Learning for Scribal Hands of Linear B