PergaNet: A Deep Learning Framework for Automatic Appearance-Based Analysis of Ancient Parchment Collections

Paolanti, Marina; Pietrini, Rocco; Sciucca, Laura Della; Balloni, Emanuele; Compagnoni, Benedetto Luigi; Cesarini, Antonella; Fois, Luca; Feliciati, Pierluigi; Frontoni, Emanuele

doi:10.1007/978-3-031-13324-4_25

Lecture Notes in Computer Science

2022

DOI: 10.1007/978-3-031-13324-4_25

|View full text |Cite

PergaNet: A Deep Learning Framework for Automatic Appearance-Based Analysis of Ancient Parchment Collections

Marina Paolanti

Rocco Pietrini

Laura Della Sciucca

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine Learning for Ancient Languages: A Survey

Sommerschield,

Assael,

Pavlopoulos

et al. 2023

Computational Linguistics

View full text Add to dashboard Cite

Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in Artificial Intelligence and Machine Learning have enabled analyses on a scale and in a detail that are reshaping the field of Humanities, similarly to how microscopes and telescopes have contributed to the realm of Science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script and medium, spanning over three and a half millennia of civilisations around the ancient world. To analyse the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitisation, restoration, attribution, linguistic analysis, textual criticism, translation and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the Humanities and Machine Learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, flagging promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the Humanities and Machine Learning.

show abstract

Machine Learning for Ancient Languages: A Survey

Sommerschield,

Assael,

Pavlopoulos

et al. 2023

Computational Linguistics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

PergaNet: A Deep Learning Framework for Automatic Appearance-Based Analysis of Ancient Parchment Collections

Cited by 1 publication

References 17 publications

Machine Learning for Ancient Languages: A Survey

Machine Learning for Ancient Languages: A Survey

Contact Info

Product

Resources

About