Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR Robert Turnbull, Evelyn Mannix First place in character recognition challenge Second place in character detection challenge Best recall and precision results for detection and recognition results for IoU 0.5 Releasing prediction results in multiple formats for 4500+ Oxyrhynchus Papyri images Abstract 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 17 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. 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. We ran our 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.