Exploring Medieval Manuscripts Writer Predictability: A Study on Scribe and Letter Identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Handwriting communication is a long-established human activity that has survived into the 21st century. Accordingly, research interest in handwritten documents, both historical and modern, is significant. The way we write has changed significantly over the past few centuries. For example, texts of the Middle Ages were often written and copied by anonymous scribes. The writing of each scribe, known as his/her "scribal hand" is unique. It can be differentiated using a variety of consciously and unconsciously produced features. Distinguishing between these different scribal hands is a central focus of the humanities research field known as "paleography." Character recognition within each scribal hand has also posed an interesting challenge. Some issues make these digital processes difficult, such as paper degradation and the soiling of the manuscript page. Thus, in this paper, we propose an investigation in both perspectives, character recognition and writer identification, in medieval manuscripts to better understand the specific behaviour of two 800-year-old scribes based on their manuscripts in comparison with a modern calligrapher. The experiments demonstrated that degradation and tremor can influence the analysis of medieval handwriting documents. However, the results presented an efficient accuracy with a better accuracy rate in letter classification than in writer identification.La communication manuscrite est une longue tradition humaine qui a persisté jusqu’à nos jours, au 21e siècle. Par conséquent, l’intérêt de la recherche concernant des documents manuscrits historiques et modernes est grand. La façon dont nous écrivons a changé au cours des derniers siècles. Par exemple, des textes du Moyen Âge ont souvent été écrits et copiés par des scribes. L’écriture de chaque scribe, appelée son « écriture scribale » (anglais scribal hand), est unique. Nous pouvons la différencier en observant une gamme de caractéristiques produites consciemment et inconsciemment. Faire la distinction entre ces écritures scribales différentes est au centre des préoccupations du domaine de recherche de paléographie. La reconnaissance de caractères dans chacune des écritures scribales pose des défis intéressants. Certains problèmes, tels que la dégradation de papier et l’encrassement de la page manuscrite, rendent difficiles ces processus numériques. Dans cet article, nous proposons ainsi une enquête sur les deux perspectives, la reconnaissance de caractères et l’identification de scribes, dans les manuscrits médiévaux dans le but de mieux comprendre le comportement spécifique de deux scribes vivant il y a 800 ans, en se basant sur leurs manuscrits en comparaison avec un calligraphe moderne. Les expériences démontrent que la dégradation et le tremblement peuvent influer sur l’analyse des documents manuscrits médiévaux. Cependant, les résultats ont présenté une précision efficace, avec un meilleur taux de précision dans la reconnaissance de caractères que dans celui de l’identification de scribe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it