Code and data (feature files, OxCal data) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis

The dataset is associated with the following article: Title: Dating ancient manuscripts using radiocarbon and AI-based writing style analysis Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar (Under review) This data set is collected for the ERC project: The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls PI: Mladen Popović Grant agreement ID: 640497 Project website: https://cordis.europa.eu/project/id/640497 Copyright (c) University of Groningen, 2023. All rights reserved. Disclaimer and copyright notice for all data contained on the *.tar.gz file: 1) permission is hereby granted to use the code for research purposes. It is not allowed to distribute this code for commercial purposes. 2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed. 3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this code. 4) the user should refer to the first public article mentioned above in this code. 5) the recipient should refrain from proliferating the code to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy. Overview: (Update on 29 July 2023: The data and the feature files are correctly added in the directories. Please use the files from this version and disregard the previous one: 10.5281/zenodo.8168930. ) This is the code for training and testing Enoch, the Bayesian regression-based date prediction model for the Dead Sea Scrolls data. The model incorporates radiocarbon data and palaeographic input to produce a probabilistic date prediction for each test manuscript. Organization of the files: The *.tar.gz file contains the main.py file that runs Enoch. All the dependencies and utility files are included as well. The README-FINAL.md explains how to run the Python code. The feature files and raining labels are included in the data directory. The image data is also available from: https://doi.org/10.5281/zenodo.8168210 System requirements: Hardware requirements: Enoch requires a standard computer with enough RAM to support the in-memory operations. Software requirements: OS Requirements: The package is tested on macOS (Ventura 13.4.1) and Linux (Ubuntu 20.04.6 LTS) Python dependencies: (included as rerquirements.txt file inside the *.tar.gz file) joblib==1.0.1 matplotlib==3.4.2 numpy==1.20.3 pandas==1.2.4 scikit_learn==0.24.2 scipy==1.6.3 tqdm==4.61.0 Installation: pip install -r requirements.txt Additional tools: Binarization: The images are already binarized and preprocessed. For additional user data or personal use, the BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl). - Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930. Image Morphing: In the original article, data augmentation was performed using image morphing. The tool is available on GitHub: https://github.com/GrHound/imagemorph.c Features for writer identification: Lambert Schomaker http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html - L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of the upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798. - Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717. If you have any questions, please get in touch with us: Maruf A. Dhali Andrei Krauss Lambert Schomaker