Twenty-five hundred years ago, the paperwork of the Achaemenid Empire was recorded on clay tablets. In 1933, archaeologists from the University of Chicago's Oriental Institute (OI) found tens of thousands of these tablets and fragments during the excavation of Persepolis. Many of these tablets have been painstakingly photographed and annotated by expert cuneiformists, and now provide a rich dataset consisting of over 5,000 annotated tablet images and 100,000 cuneiform sign bounding boxes. We leverage this dataset to develop DeepScribe, a modular computer vision pipeline capable of localizing cuneiform signs and providing suggestions for the identity of each sign. We investigate the difficulty of learning subtasks relevant to cuneiform tablet transcription on ground-truth data, finding that a RetinaNet object detector can achieve a localization mAP of 0.78 and a ResNet classifier can achieve a top-5 sign classification accuracy of 0.89. The end-to-end pipeline achieves a top-5 classification accuracy of 0.80. As part of the classification module, DeepScribe groups cuneiform signs into morphological clusters. We consider how this automatic clustering approach differs from the organization of standard, printed sign lists and what we may learn from it. These components, trained individually, are sufficient to produce a system that can analyze photos of cuneiform tablets from the Achaemenid period and provide useful transliteration suggestions to researchers. We evaluate the model's end-to-end performance on locating and classifying signs, providing a roadmap to a linguistically-aware transliteration system, then consider the model's potential utility when applied to other periods of cuneiform writing.

翻译：两千五百年前，阿契美尼德帝国的文书被记录在泥板上。1933年，芝加哥大学东方研究所（OI）的考古学家在波斯波利斯遗址的发掘中发现了数万块此类泥板及碎片。其中许多泥板已由楔形文字专家悉心拍摄并标注，现形成包含5000余幅标注泥板图像及10万个楔形文字符号边界框的丰富数据集。我们利用该数据集开发了DeepScribe——一套模块化计算机视觉流水线，能够定位楔形文字符号并对其身份提供分类建议。本研究基于真实标注数据，探究了与泥板转写相关的子任务学习难度：经测试，RetinaNet目标检测器的定位平均精度（mAP）达0.78，ResNet分类器的符号前5分类准确率达0.89，端到端流水线的前5分类准确率达0.80。作为分类模块的组成部分，DeepScribe将楔形文字符号按形态聚类。我们探讨了这种自动聚类方式与标准印刷符号列表组织方式的差异及其潜在启示。各模块经独立训练后，足以构成能分析阿契美尼德时期楔形文字泥板照片、并为研究者提供有效转写建议的系统。我们评估了模型在符号定位与分类任务上的端到端性能，由此勾勒出具有语言学感知能力的转写系统发展路线图，并进一步探讨了该模型应用于其他时期楔形文字文本的潜在价值。