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The study of ancient documents provides a glimpse into our past. However, thelow image quality and intricate details commonly found in these documentspresent significant challenges for accurate object detection. The objective ofthis research is to enhance object detection in ancient documents by reducingfalse positives and improving precision. To achieve this, we propose a methodthat involves the creation of synthetic datasets through computationalmediation, along with the integration of visual feature extraction into theobject detection process. Our approach includes associating objects with theircomponent parts and introducing a visual feature map to enable the model todiscern between different symbols and document elements. Through ourexperiments, we demonstrate that improved object detection has a profoundimpact on the field of Paleography, enabling in-depth analysis and fostering agreater understanding of these valuable historical artifacts.

Enhancing Object Detection in Ancient Documents with Synthetic Data Generation and Transformer-Based Models