  Standard natural language processing (NLP) pipelines operate on symbolic
representations of language, which typically consist of sequences of discrete
tokens. However, creating an analogous representation for ancient logographic
writing systems is an extremely labor intensive process that requires expert
knowledge. At present, a large portion of logographic data persists in a purely
visual form due to the absence of transcription -- this issue poses a
bottleneck for researchers seeking to apply NLP toolkits to study ancient
logographic languages: most of the relevant data are images of writing.
  This paper investigates whether direct processing of visual representations
of language offers a potential solution. We introduce LogogramNLP, the first
benchmark enabling NLP analysis of ancient logographic languages, featuring
both transcribed and visual datasets for four writing systems along with
annotations for tasks like classification, translation, and parsing. Our
experiments compare systems that employ recent visual and text encoding
strategies as backbones. The results demonstrate that visual representations
outperform textual representations for some investigated tasks, suggesting that
visual processing pipelines may unlock a large amount of cultural heritage data
of logographic languages for NLP-based analyses.
