用目标检测识别古罗马硬币文字，准确率达90.4%
Reading Legends on Ancient Coins: An Object Detection Approach for Character Recognition on a Novel Roman Republican Dataset
- 用YOLO系列模型检测硬币上的文字位置
- 在38,808个标注上实现90.4%的检测精度
- 适合历史考古与数字人文研究者
古代硬币的文本铭文（即铭文）对确定其年代和发行者至关重要。这些铭文由仍用于英语的字母组成。本文基于深度学习的目标检测方法，解决古罗马共和国时期硬币图像中的字符识别问题。由于铭文位置不均、雕刻技术原始及磨损严重，加上光照、方向、尺度等成像条件不一，识别难度高。为此，我们构建了一个包含5,654张硬币图像的新颖大规模数据集，手动标注21种字符标签，共38,808个标注。采用YOLOv3、v4、v5、v7和v8等变体进行识别，其中YOLOv7-Large取得最高mAP50为90.4%，其次为YOLOv7-Extended（90.2%）和YOLOv7-xl（90.1%）。
原文摘要 · Abstract (English)
When it comes to the proper classification of ancient coins with respect to their time and issuer, the textual inscriptions on these coins, also known as legends, are of paramount importance. These legends consist of alphabets or characters still used in English. This paper addresses image based character recognition on ancient Roman Republican coins via a deep learning based object detection strategy. However, legends on these coins pose high variation due to non-uniform placement, primitive inscription techniques, and wear and tear. Additional challenges include inconsistent imaging conditions such as illumination, orientation, and scale. To accommodate these, we gathered a novel large-scale dataset of 5,654 Roman Republican coin images, manually annotated with 21 character labels, totaling 38,808 annotations. For recognition, we use You Only Look Once (YOLO) variants: YOLOv3, v4, v5, v7, and v8. YOLOv7-Large achieves the best mAP50 of 90.4%, followed by YOLOv7-Extended and YOLOv7-xl with 90.2% and 90.1%, respectively.
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