Faculté des Sciences appliquées
Faculté des Sciences appliquées

Master's Thesis : Papyrus matching using machine learning

Nicolas, Pierre-Loup ULiège
Promotor(s) : Geurts, Pierre ULiège ; Marée, Raphaël ULiège
Date of defense : 7-Sep-2020/9-Sep-2020 • Permalink :
Title : Master's Thesis : Papyrus matching using machine learning
Author : Nicolas, Pierre-Loup ULiège
Date of defense  : 7-Sep-2020/9-Sep-2020
Advisor(s) : Geurts, Pierre ULiège
Marée, Raphaël ULiège
Committee's member(s) : Van Droogenbroeck, Marc ULiège
Polis, Stéphane ULiège
Language : English
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en science des données, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées


[fr] For papyrologists, the task of reconstructing papyri from scattered fragments is a tedious and time-consuming, yet crucial task to uncover new texts that could provide meaningful information on a variety of subjects.
With the advent of digital humanities, several tools have been developed to make papyrological studies more effective, in particular when it comes to the digitization and manipulation of papyri. But none of these tools can help papyrologists for the decision-making involved in the reconstruction of papyri.
In recent years, it has been shown that deep learning techniques can be successfully applied to a wide variety of problems, sometimes with impressive results. However, the application of such techniques to papyrology is pretty much novel.

In this thesis, deep learning techniques are applied to the task of papyrus fragment matching, to evaluate the possibilities offered by neural networks to tackle this complex task. Using a papyrus fragment dataset built from scratch using raw data from the papyrus collection of the Museo Egizio of Turin, several siamese neural network architectures are evaluated and compared.
The best performing model is also evaluated on more a realistic use case and gives some encouraging results, though it is not yet ready for practical use. Several improvements are proposed to further improve the results.



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  • Nicolas, Pierre-Loup ULiège Université de Liège > Mast. sc. don. à fin.


Committee's member(s)

  • Van Droogenbroeck, Marc ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
    ORBi View his publications on ORBi
  • Polis, Stéphane ULiège Université de Liège - ULiège > Département des sciences de l'antiquité > Egyptologie
    ORBi View his publications on ORBi
  • Total number of views 73
  • Total number of downloads 12

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