The study of ancient documents provides a glimpse into our past. However, the
low image quality and intricate details commonly found in these documents
present significant challenges for accurate object detection. The objective of
this research is to enhance object detection in ancient documents by reducing
false positives and improving precision. To achieve this, we propose a method
that involves the creation of synthetic datasets through computational
mediation, along with the integration of visual feature extraction into the
object detection process. Our approach includes associating objects with their
component parts and introducing a visual feature map to enable the model to
discern between different symbols and document elements. Through our
experiments, we demonstrate that improved object detection has a profound
impact on the field of Paleography, enabling in-depth analysis and fostering a
greater understanding of these valuable historical artifacts.

通过降低虚假阳性和提高精确度的方法，我们在古代文献中增强了物体检测，并通过计算转换生成合成数据集，将视觉特征提取与物体检测过程相结合，进一步关联物体与其组成部分，并引入视觉特征图，从而深入分析和促进了有价值的历史文物，给古文字学领域带来了深远影响。