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. 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.

翻译：从纸莎草手稿摹本图像中分离并识别单个字符的能力为数字分析提供了丰富机遇。为此，作为第17届国际文档分析与识别会议的一部分，举办了"ICDAR 2023纸莎草文献希腊字母检测与识别竞赛"。本文讨论了我们提交至该竞赛的方案。我们采用YOLOv8模型集成进行字符检测与分类，并运用两种不同策略优化字符预测结果：基于Transformer的DeiT方法，以及通过自监督学习方法SimCLR在大规模无标注语料上训练的ResNet-50模型。我们的提交方案在识别任务中以42.2%的平均精度（mAP）夺冠，在检测任务中以51.4%的平均精度（mAP）获得亚军。在更宽松的交并比阈值0.5条件下，我们同时取得了检测与分类任务的最高平均精度与平均召回率。我们利用该预测流程对来自Oxyrhynchus纸莎草文献的4500余幅图像进行分析以验证方法实用性，并以多种格式公开发布了分析结果。