Enhancing Object Detection in Ancient Documents with Synthetic Data Generation and Transformer-Based Models
This work addresses challenges in Paleography by enabling more accurate analysis of historical artifacts, though it appears incremental as it builds on existing synthetic data and transformer-based techniques.
The research tackled the problem of low image quality and intricate details in ancient documents by proposing a method to enhance object detection, resulting in reduced false positives and improved precision.
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.