# Segmentation of Ink and Parchment in Dead Sea Scroll Fragments

arXiv: https://arxiv.org/abs/2411.10668 · HTML: https://mlsanity.com/paper/2411.10668/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [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)
- [Image-based material analysis of ancient historical documents](https://mlsanity.com/paper/2203.01042/) (2022)
- [The Effects of Character-Level Data Augmentation on Style-Based Dating of Historical Manuscripts](https://mlsanity.com/paper/2212.07923/) (2022)
- [Segmenting Dead Sea Scroll Fragments for a Scientific Image Set](https://mlsanity.com/paper/2406.15692/) (2024)

## Abstract (arXiv)

The discovery of the Dead Sea Scrolls over 60 years ago is widely regarded as one of the greatest archaeological breakthroughs in modern history. Recent study of the scrolls presents ongoing computational challenges, including determining the provenance of fragments, clustering fragments based on their degree of similarity, and pairing fragments that originate from the same manuscript -- all tasks that require focusing on individual letter and fragment shapes. This paper presents a computational method for segmenting ink and parchment regions in multispectral images of Dead Sea Scroll fragments. Using the newly developed Qumran Segmentation Dataset (QSD) consisting of 20 fragments, we apply multispectral thresholding to isolate ink and parchment regions based on their unique spectral signatures. To refine segmentation accuracy, we introduce an energy minimization technique that leverages ink contours, which are more distinguishable from the background and less noisy than inner ink regions. Experimental results demonstrate that this Multispectral Thresholding and Energy Minimization (MTEM) method achieves significant improvements over traditional binarization approaches like Otsu and Sauvola in parchment segmentation and is successful at delineating ink borders, in distinction from holes and background regions.