ProtoSnap: Prototype Alignment for Cuneiform Signs
Journal:
arXiv
Published Date:
Jan 31, 2025
Abstract
The cuneiform writing system served as the medium for transmitting knowledge
in the ancient Near East for a period of over three thousand years. Cuneiform
signs have a complex internal structure which is the subject of expert
paleographic analysis, as variations in sign shapes bear witness to historical
developments and transmission of writing and culture over time. However, prior
automated techniques mostly treat sign types as categorical and do not
explicitly model their highly varied internal configurations. In this work, we
present an unsupervised approach for recovering the fine-grained internal
configuration of cuneiform signs by leveraging powerful generative models and
the appearance and structure of prototype font images as priors. Our approach,
ProtoSnap, enforces structural consistency on matches found with deep image
features to estimate the diverse configurations of cuneiform characters,
snapping a skeleton-based template to photographed cuneiform signs. We provide
a new benchmark of expert annotations and evaluate our method on this task. Our
evaluation shows that our approach succeeds in aligning prototype skeletons to
a wide variety of cuneiform signs. Moreover, we show that conditioning on
structures produced by our method allows for generating synthetic data with
correct structural configurations, significantly boosting the performance of
cuneiform sign recognition beyond existing techniques, in particular over rare
signs. Our code, data, and trained models are available at the project page:
https://tau-vailab.github.io/ProtoSnap/
