SALAMI - Subjective Assessments of Legibility in Ancient Manuscript Images

<strong>This is just a preview containing data for 5 test images (out of 250) for review purposes; the full dataset will be published when the accompanying paper is accepted.</strong> 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.<br> 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. <strong>Description of files:</strong> images input - rated test images mean_score_maps - spatial maps of mean legibility std_maps - spatial maps of uncertainty (standard deviation of legibility) src images.json - definition of source images contained in the dataset users.json - list of participants with their respective properties assessments.json - the main data generated by our experiments. salami_proc.py - contains python functions to process the .json files named above salami_proc_usage.py - uses the functions from salami_proc.py to reproduce the output images and statistical results described in the accompanying paper salami_llm.R - documents the linear mixed models analysis performed in R