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.

翻译：目的：从纸莎草手稿的传真图像中分离并识别单个字符的能力为数字分析带来了丰富机遇。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纸莎草希腊字母检测与识别竞赛"。本文讨论了我们提交的竞赛方案。方法：我们采用YOLOv8模型集成来检测和分类单个字符，并运用两种不同方法优化字符预测，包括基于Transformer的DeiT方法，以及使用自监督学习方法SimCLR在大量未标注数据上训练的ResNet-50模型。结果：我们的方案在识别挑战中以42.2%的平均精度均值（mAP）夺冠，在检测挑战中以51.4%的mAP获得亚军。在更宽松的交并比阈值0.5下，我们在检测和分类任务中均取得了最高的平均精度均值和平均召回率均值。结论：这些结果展示了该技术用于历史手稿自动字符识别的潜力。我们对奥克西林库斯纸莎草文献中超过4500张图像运行预测流程以验证方法实用性，并公开提供了多种格式的结果数据。