# Image-based material analysis of ancient historical documents

arXiv: https://arxiv.org/abs/2203.01042 · HTML: https://mlsanity.com/paper/2203.01042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 2 Mar 2022.

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

- [Material Recognition in the Wild with the Materials in Context Database](https://mlsanity.com/paper/1412.0623/) (2014)
- [Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)](https://mlsanity.com/paper/2010.14476/) (2020)
- [BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks](https://mlsanity.com/paper/1911.07930/) (2019)
- [Fourier-CPPNs for Image Synthesis](https://mlsanity.com/paper/1909.09273/) (2019)
- [Evaluating Deep Convolutional Neural Networks for Material Classification](https://mlsanity.com/paper/1703.04101/) (2017)

## Built on by (most cited first, 1 of 1)

- [Segmentation of Ink and Parchment in Dead Sea Scroll Fragments](https://mlsanity.com/paper/2411.10668/) (2024)

## Abstract (arXiv)

Researchers continually perform corroborative tests to classify ancient historical documents based on the physical materials of their writing surfaces. However, these tests, often performed on-site, requires actual access to the manuscript objects. The procedures involve a considerable amount of time and cost, and can damage the manuscripts. Developing a technique to classify such documents using only digital images can be very useful and efficient. In order to tackle this problem, this study uses images of a famous historical collection, the Dead Sea Scrolls, to propose a novel method to classify the materials of the manuscripts. The proposed classifier uses the two-dimensional Fourier Transform to identify patterns within the manuscript surfaces. Combining a binary classification system employing the transform with a majority voting process is shown to be effective for this classification task. This pilot study shows a successful classification percentage of up to 97% for a confined amount of manuscripts produced from either parchment or papyrus material. Feature vectors based on Fourier-space grid representation outperformed a concentric Fourier-space format.