Enhancing Object Detection in Ancient Documents with Synthetic Data
  Generation and Transformer-Based Models
Zahra Ziran Francesco Leotta Massimo Mecella
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.