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This paper introduces neural approaches for reversing character-set simplification and abbreviation in medieval text, using one-to-one and banded RNNs trained with self-supervision or parallel corpora, and presents a Python library for letter lemmatization.
This paper presents a transformer-based architecture with prototype learning that enables scalable paleographic measurements from historical documents using only line-level transcriptions, demonstrating effectiveness on a 160-page codex with minimal training data.