  Purpose: The capacity to isolate and recognize individual characters from
facsimile images of papyrus manuscripts yields rich opportunities for digital
analysis. For this reason the `ICDAR 2023 Competition on Detection and
Recognition of Greek Letters on Papyri' was held as part of the 17th
International Conference on Document Analysis and Recognition. This paper
discusses our submission to the competition.
  Methods: We used an ensemble of YOLOv8 models to detect and classify
individual characters and employed two different approaches for refining the
character predictions, including a transformer based DeiT approach and a
ResNet-50 model trained on a large corpus of unlabelled data using SimCLR, a
self-supervised learning method.
  Results: Our submission won the recognition challenge with a mAP of 42.2%,
and was runner-up in the detection challenge with a mean average precision
(mAP) of 51.4%. At the more relaxed intersection over union threshold of 0.5,
we achieved the highest mean average precision and mean average recall results
for both detection and classification.
  Conclusion: The results demonstrate the potential for these techniques for
automated character recognition on historical manuscripts. We ran the
prediction pipeline on more than 4,500 images from the Oxyrhynchus Papyri to
illustrate the utility of our approach, and we release the results publicly in
multiple formats.
