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

Source: [Enhancing Object Detection in Ancient Documents with Synthetic Data Generation and Transformer-Based Models](<https://www.sota2.com/research/paper/68fa70251e209131cea495b4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:35:15.802Z

Authors: Zahra Ziran, Francesco Leotta, Massimo Mecella

Published: 2023-07-29T15:29:25.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.16005>)

[PDF](<https://arxiv.org/pdf/2307.16005.pdf>)