Edizioni Ca' Foscari - Venice University Press

Abstract The new digital technologies have become an effective tool for researchers in different fields. Historians and archaeologists who are studying Greek and Roman Libya have benefited from technical developments in presenting different kinds of data, particularly relating to the epigraphy and toponymy of Libya. They have recently published several resources, and are working on more. This study presents the story of how scholars have collected a variety of Libyan heritage materials and published them online; the account makes it clear that these digital projects are the result of extensive and ongoing collaboration between researchers from different countries, including Libya. They have worked together, and are still working to produce valuable online corpora of inscriptions alongside the Heritage Gazetteer of Libya which records names used at different times, and in a variety of languages, of heritage sites. We also discuss plans for further improving the accessibility of these materials, and encouraging their wider use. , أصبحت التقنيات الرقمية الحديثة أداة فعالة للباحثين في مجالات مختلفة، وقد استفاد المؤرخون والآثاريون، المهتمون بتاريخ ليبيا وآثارها في الفترتين الإغريقية والرومانية، من التطورات التقنية لتقديم بيانات (data) مختلفة وخاصة تلك المتعلقة بالنقوش ودراسة مسميات المواقع الأثرية بليبيا. قام هؤلاء حديثاً بنشر مصادر متعددة، معضمها لنقوش، ولا يزال العمل مستمر على أعمال أخرى. توضح هذه الدراسة الكيفية التي تمكن خلالها الباحثون من جمع مواد متنوعة من التراث الليبي، ثم نشرها لتكون متاحة للجميع على الشبكة العنكبوتية (الإنترنت). تُبين هذه الورقة بشكل جلي أن هذه المشاريع الرقمية جاءت نتيجة تعاون مكثف ودؤوب بين باحثين من بلدان عِدَّة بما فيها ليبيا. حيث عملوا معاً ولا يزالوا مستمرين في تقديم مجموعة قيمة للنقوش الإغريقية واللاتينية، بالإضافة إلى فهرس جغرافي يسجل اسماء لمواقع أثرية اُستخدمت في حقب مختلفة وسُميت بلغات متعددة. كما ناقشت هذه الورقة خططاً من شأنها تطويرإمكانية الوصول لهذه المواد التراثية وتشجيع استخدامها على نطاق موسع .

Questo contributo illustra un’efficace esperienza di collaborazione tra diverse istituzioni, quali enti di ricerca, di tutela e formazione, e differenti professionalitá, coinvolte nell’ambito dell’alternanza scuola-lavoro, ai fini della digitalizzazione e valorizzazione del patrimonio epigrafico del Museo Civico Castello Ursino del Comune di Catania. Il Museo possiede una rilevante collezione epigrafica, costituita da due raccolte settecentesche catanesi. Alcune iscrizioni sono esposte secondo i vecchi criteri di fruizione museale; la maggior parte é custodita in deposito. Uno degli obiettivi dell’esperienza é dunque far conoscere al pubblico fruitore ed alla comunitá degli studiosi l’ingente patrimonio epigrafico del Castello attualmente non esposto al pubblico. Il progetto di durata triennale é stato caratterizzato in un primo momento da una attivitá di digitalizzazione di schede catalografiche e di documentazione grafica e fotografica di parte delle epigrafi; durante il secondo anno di attivit. é stata realizzata una esposizione presso il Castello Ursino di un gruppo selezionato di epigrafi di provenienza catanese, che sono state sottoposte a un primo restauro conservativo ed inserite nel catalogo digitale con la relativa documentazione. Le epigrafi sono codificate secondo standard e vocabolari controllati internazionali (formatto EpiDoc TEI XML, Pleiades per posizioni geografiche e lessici controllati di EAGLE per i tipi di iscrizioni, i materiali e i supporti) al fine di consentire la creazione di Linked Open Data. La mostra presenta, tra l’altro, la realizzazione di due video e l’inserimento di un chiosco digitale multimediale per accedere a contenuti di approfondimento e immagini 3D. Durante il terzo anno é programmata la prosecuzione dell’attivit. di digitalizzazione e documentazione ed un approfondimento della disseminazione dei risultati sul web.

Epigraphy is witnessing a growing integration of artificial intelligence, notably through its subfield of machine learning (ML), especially in tasks like extracting insights from ancient inscriptions. However, scarce labeled data for training ML algorithms severely limits current techniques, especially for ancient scripts like Old Aramaic. Our research pioneers an innovative methodology for generating synthetic training data tailored to Old Aramaic letters. Our pipeline synthesizes photo-realistic Aramaic letter datasets, incorporating textural features, lighting, damage, and augmentations to mimic real-world inscription diversity. Despite minimal real examples, we engineer a dataset of 250 000 training and 25 000 validation images covering the 22 letter classes in the Aramaic alphabet. This comprehensive corpus provides a robust volume of data for training a residual neural network (ResNet) to classify highly degraded Aramaic letters. The ResNet model demonstrates 95% accuracy in classifying real images from the 8th century BCE Hadad statue inscription. Additional experiments validate performance on varying materials and styles, proving effective generalization. Our results validate the model’s capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis. Our innovative framework elevates interpretation accuracy on damaged inscriptions, thus enhancing knowledge extraction from these historical resources.

Human history is born in writing. Inscriptions are among the earliest written forms, and offer direct insights into the thought, language and history of ancient civilizations. Historians capture these insights by identifying parallels—inscriptions with shared phrasing, function or cultural setting—to enable the contextualization of texts within broader historical frameworks, and perform key tasks such as restoration and geographical or chronological attribution. However, current digital methods are restricted to literal matches and narrow historical scopes. Here we introduce Aeneas, a generative neural network for contextualizing ancient texts. Aeneas retrieves textual and contextual parallels, leverages visual inputs, handles arbitrary-length text restoration, and advances the state of the art in key tasks. To evaluate its impact, we conduct a large study with historians using outputs from Aeneas as research starting points. The historians find the parallels retrieved by Aeneas to be useful research starting points in 90% of cases, improving their confidence in key tasks by 44%. Restoration and geographical attribution tasks yielded superior results when historians were paired with Aeneas, outperforming both humans and artificial intelligence alone. For dating, Aeneas achieved a 13-year distance from ground-truth ranges. We demonstrate Aeneas’ contribution to historical workflows through analysis of key traits in the renowned Roman inscription Res Gestae Divi Augusti, showing how integrating science and humanities can create transformative tools to assist historians and advance our understanding of the past.

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 civilizations1. 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. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

The ERC project DASI is aimed at digitizing the overall epigraphic heritage of the ancient Arabian peninsula, in order to enhance knowledge of the pre-Islamic Arabian languages and cultures. This paper describes the challenges faced and the solutions proposed in the construction of a digital lexicon tool for under-resources languages such as those attested in the epigraphic documentation of pre-Islamic Arabia.

DASI is an ERC-Advanced Grant project aimed at digitizing the pre-Islamic inscriptions from Arabia and fostering best practices for the digitization of the epigraphic heritage related to Semitic languages. This paper describes the content model, the standards chosen, and exemplifies the vocabularies in view of a possible harmonization of data pertaining to the specific domain. The architecture of the system and the tools for encoding and retrieving textual content are also illustrated.

The ERC-Advanced Grant project DASI has contributed to define and foster best practices in the digitization of pre-Islamic inscriptions Arabian inscriptions. As one of the early attempts at digitizing the epigraphic heritage related to Semitic languages, it has been facing specific challenges in support description and text encoding. This contribute describes the solutions chosen to encode and represent different kinds of phenomena, such as phonemes typical of the Semitic languages, onomastics, textual portions, symbols and grammatical phenomena. Moreover a digital lexicon tool for under-resources languages, such as those attested in the epigraphic documentation of pre-Islamic Arabia, is illustrated.

The paper describes the main challenges faced, and the solutions adopted in the frame of the project DASI - Digital Archive for the study of pre-Islamic Arabian inscriptions. In particular, the methodological and technological issues emerged in the conversion from a domain-specific text-based project of digital edition of an epigraphic corpus, to an objective-driven archive for the study and dissemination of inscriptions in different languages and scripts are discussed. With a view to keeping pace with, and possibly fostering reasoning on best practices in the community of digital epigraphers beyond each specific cultural/linguistic domain, special attention is devoted to: the modelling of data and encoding (XML annotation vs database approach; the conceptual model for the valorization of the material aspect of the epigraph; the textual encoding for critical editions); interoperability (pros and cons of compliance to standards; harmonization of metadata; openness; semantic interoperability); lexicography (tools for under-resourced languages; translations).

The Digital Archive for the Study of Pre-Islamic Arabian Inscriptions (DASI) is a five-year ERC project of the University of Pisa, directed by Prof. A. Avanzini. Started in May 2011, the project seeks to collect the whole corpus of pre-Islamic Arabian inscriptions in an open-access archive, with the aim of fostering studies and scientific publications on the epigraphic heritage of Arabia. The paper describes the main activities carried out in the first two years of the project: the IT research on the cataloguing methodologies of the epigraphic material, the digitization of thousands of pre-Islamic Arabian inscriptions, and the setting up of the archive website for the fruition of the catalogued material, which opened in October 2013. The project also encourages the involvement of international partners and promotes interest in pre-Islamic Arabia through a series of related activities and projects, such as the IVIEDINA digital library and the IMTO archaeological database, which are promoted in the Arabia Antica portal of the University of Pisa.