# Subjective Assessments of Legibility in Ancient Manuscript Images -- The SALAMI Dataset

arXiv: https://arxiv.org/abs/2102.09961 · HTML: https://mlsanity.com/paper/2102.09961/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts](https://mlsanity.com/paper/1612.06457/) (2016)

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

- [Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions](https://mlsanity.com/paper/2104.01106/) (2021)
- [Reconstructing Historical Manuscripts through MSI: The Potential of Contrast in Assessing Image Quality and Legibility](https://mlsanity.com/paper/2608.13381/) (2026)

## Abstract (arXiv)

The research field concerned with the digital restoration of degraded written heritage lacks a quantitative metric for evaluating its results, which prevents the comparison of relevant methods on large datasets. Thus, we introduce a novel dataset of Subjective Assessments of Legibility in Ancient Manuscript Images (SALAMI) to serve as a ground truth for the development of quantitative evaluation metrics in the field of digital text restoration. This dataset consists of 250 images of 50 manuscript regions with corresponding spatial maps of mean legibility and uncertainty, which are based on a study conducted with 20 experts of philology and paleography. As this study is the first of its kind, the validity and reliability of its design and the results obtained are motivated statistically: we report a high intra- and inter-rater agreement and show that the bulk of variation in the scores is introduced by the images regions observed and not by controlled or uncontrolled properties of participants and test environments, thus concluding that the legibility scores measured are valid attributes of the underlying images.