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

arXiv: https://arxiv.org/abs/2307.16005 · HTML: https://mlsanity.com/paper/2307.16005/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 29 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Towards a Neural Statistician](https://mlsanity.com/paper/1606.02185/) (2016)
- [Stacked Capsule Autoencoders](https://mlsanity.com/paper/1906.06818/) (2019)
- [Formal Algorithms for Transformers](https://mlsanity.com/paper/2207.09238/) (2022)

## Built on by (most cited first, 2 of 2)

- [Studying Illustrations in Manuscripts: An Efficient Deep-Learning Approach](https://mlsanity.com/paper/2601.05269/) (2025)
- [A Visual Classification Dataset and Model Evaluation for Historical Manuscript Illustrations](https://mlsanity.com/paper/2609.33449/) (2026)

## Abstract (arXiv)

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.