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Humanist Archives: March 13, 2022, 7:19 a.m. Humanist 35.590 - neural networks for ancient texts

              Humanist Discussion Group, Vol. 35, No. 590.
        Department of Digital Humanities, University of Cologne
                      Hosted by DH-Cologne
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        Date: 2022-03-12 14:36:51+00:00
        From: maurizio lana <>
        Subject: Re: [Humanist] 35.587: pubs: paper on neural networks for ancient texts

hi Thea,

your article says a lot of really innovative things both on the
methodological and on the expositive/design level (how many times we
read articles or listen to talk about projects that are not clear if
and how they are able to change for the better the research
perspectives?). but also this project is the second in which in the
space of 20 days, i meet a framework of AI trained with machine
learning, for an advanced DH activity developable by an individual
using a personal device.

this is what i would call "the hidden content" of your article: that
(most) probably the training of the digital humanist for the years
to come must include training in what machine learning is, and how
it can be done, and in what AI framework, for a DH project.
a bit like when 30 years ago you had to know and know how to use PCA
(principal component analysis).


Il 11/03/22 07:19, Humanist ha scritto

Date: 2022-03-10 10:31:05+00:00
From: Thea Sommerschield <thea.sommerschield@CLASSICS.OX.AC.UK>
Subject: New tool for restoring and attributing Greek inscriptions using
Artificial Intelligence

Dear List Members,

Today Nature publishes our paper
<> “Restoring and
attributing ancient texts using deep neural networks”. The paper
introduces Ithaca: the first assistive tool using deep neural networks
to aid historians in not only restoring the missing text of ancient
Greek inscriptions, but also identifying their original location, and
establishing the date they were written.

This work is a collaboration between the Universities of Venice Ca’
Foscari <>, Oxford
<>and Athens AUEB
<>, and Google’s DeepMind <>.
To make this research widely available to researchers, educators, museum
staff and others, we partnered with Google Cloud
<>and Google Arts & Culture
<>to launch a interactive version
<>of Ithaca for historians to use for their
personal research.

Please find the abstract of the published paper below:

“Ancient History relies on disciplines such as Epigraphy, the study of
inscribed texts known as "inscriptions", for evidence of the thought,
language, society and history of past civilizations. However, over the
centuries many inscriptions have been damaged to the point of
illegibility, transported far from their original location, and their
date of writing is steeped in uncertainty.

We present Ithaca, the first Deep Neural Network for the textual
restoration, geographical and chronological attribution of ancient Greek
inscriptions. Ithaca is designed to assist and expand the historian’s
workflow: its architecture focuses on collaboration, decision support,
and interpretability. While Ithaca alone achieves 62% accuracy when
restoring damaged texts, as soon as evaluated historians use Ithaca
their accuracy leaps to 72%, confirming this synergistic research aid’s
impact. Ithaca can attribute inscriptions to their original location
with 71% accuracy and can date them with a distance of less than 30
years of their ground-truth ranges, redating key texts of Classical
Athens and contributing to topical debates in Ancient History. This work
shows how models like Ithaca can unlock the cooperative potential
between AI and historians, transformationally impacting the way we study
and write about one of the most significant periods in human history.”

* Link to published paper (open access):
<> and /Nature/'s
cover issue


* Link to Ithaca’s free online interface:

* Link to the /Nature/ video:

* Link to the open source code:

* Ithaca's authors: Yannis Assael*, Thea Sommerschield*, Brendan
Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita
Chatzipanagiotou, Ion Androutsopoulos, Jonathan Prag & Nando de Freitas

Thank you for circulating, and we look forward to your feedback on the tool!

Thea Sommerschield
Marie Skłodowska-Curie Fellow | Università Ca’ Foscari Venezia
Fellow in Hellenic Studies | CHS, Harvard University

Maurizio Lana
Dipartimento di Studi Umanistici
Università del Piemonte Orientale
piazza Roma 36 - 13100 Vercelli
tel. +39 347 7370925

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