L'Intelligenza Artificiale nel flusso di lavoro dell'archivistica e della ricerca storica: HTS e ChatGPT
10.6092/issn.2532-8816/17205
Questo articolo esamina l'impatto dell'intelligenza artificiale sui processi di digitalizzazione del patrimonio archivistico, in particolare per quanto riguarda la trascrizione automatica dei manoscritti, la loro correzione e normalizzazione. Evidenzia come la digitalità abbia costretto gli studiosi a ridefinire il campo dell'archivio e della storia e abbia facilitato l'accessibilità delle fonti analogiche attraverso la digitalizzazione e l'integrazione nei big data. Lo studio si concentra su due sistemi di intelligenza artificiale, Transkribus e ChatGPT, che consentono un'analisi e una trascrizione efficiente delle fonti digitalizzate. L'articolo presenta un test di ChatGPT, utilizzato per normalizzare il testo di 366 lettere conservate nella sezione "Corrispondenza" dell'Archivio Biscari (Catania). Sebbene l'IA abbia mostrato alcune limitazioni che hanno dato luogo a imprecisioni, i testi corretti hanno soddisfatto le aspettative. Nel complesso, l'articolo conclude che la digitalizzazione e l'IA possono migliorare significativamente la ricerca archivistica e storica, consentendo l'analisi di grandi quantità di dati e l'applicazione di strumenti linguistici computazionali.
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Salvatore Spina
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Umanistica Digitale
Text Normalization
Artificial Intelligence in archival and historical scholarship workflow: HTS and ChatGPT
Text.Serial.Journal
https://umanisticadigitale.unibo.it/
Articles
2023-12-14
2023-06-13
2023-12-14
2023-12-14
125-140
Um. Dig.
Salvatore Spina
2023/12/14
10.6092/issn.2532-8816/17205
125
Università degli Studi di Catania
2532-8816
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Text Normalization
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140
Artificial Intelligence in archival and historical scholarship workflow: HTS and ChatGPT
https://umanisticadigitale.unibo.it/article/download/17205/17043
    Umanistica Digitale – ISSN: 2532-8816 – n. 16, 2023
S. Spina – Artificial Intelligence in archival and historical scholarship workflow: HTS an ChatGPT
DOI: http://doi.org/10.6092/issn.2532-8816/17205


Artificial Intelligence in archival and historical scholarship
                workflow: HTS and ChatGPT

                                                         Salvatore Spina

                                                Università degli Studi di Catania
                                               salvatore.spina@unict.it




          Abstract

The article deals with the impact of artificial intelligence tools on the archival heritage digitisation
workflow, specifically regarding the manuscripts’ automatic transcription, correction, and
normalisation. It highlights how digitality leads scholars to redefine the paradigms of archival
and historical research methodology and has facilitated the accessibility of analogue sources
through digitisation and integration into Big Data. The article focuses on two AI platforms,
Transkribus and ChatGPT, which enable efficient analysis and transcription of digitised sources,
moreover deals with a test on ChatGPT, which was employed to normalise the text of 366 letters
preserved in the “Correspondence” section of the Biscari Archive (Catania). Although, the AI
tool exhibited some limitations, which resulted in inaccuracies, the corrected texts met the
expected results. The article concludes that digitisation and AI tools can significantly enhance
archival and historical research by allowing the analysis of vast amounts of data and the
application of computational linguistic tools.
Keywords: Digital History, Semantic, Syntactic, Historical Craft, Text Normalization.
L'articolo tratta dell'impatto degli strumenti di intelligenza artificiale sul flusso di lavoro della digitalizzazione del patrimonio
archivistico, in particolare per quanto riguarda la trascrizione automatica, la correzione e la normalizzazione dei manoscritti.
Evidenzia come la digitalità porti gli studiosi a ridefinire i paradigmi della metodologia di ricerca archivistica e storica e abbia
facilitato l'accessibilità delle fonti analogiche attraverso la digitalizzazione e l'integrazione nei Big Data. L'articolo si concentra su
due piattaforme di IA, Transkribus e ChatGPT, che consentono un'analisi e una trascrizione efficiente delle fonti digitalizzate,
e tratta inoltre di un test su ChatGPT, che è stato impiegato per normalizzare il testo di 366 lettere conservate nella sezione
"Corrispondenza" dell'Archivio Biscari (Catania). Sebbene lo strumento di IA abbia mostrato alcuni limiti, che hanno portato
a delle imprecisioni, i testi corretti hanno raggiunto i risultati attesi. L'articolo conclude che la digitalizzazione e gli strumenti di
IA possono migliorare significativamente la ricerca archivistica e storica, consentendo l'analisi di grandi quantità di dati e
l'applicazione di strumenti linguistici computazionali.
Parole chiave: storia digitale, semantica, sintattica, artigianato storico, normalizzazione del testo



          Introduction

Like every system of communication and cultural experience, Humanities are being drawn into
the world of computing, and the digital turn has pushed scholars to redefine Languages, Arts,
and History paradigms. Just as words have been assimilated into “second-order abstractions”
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(i.e. numbers) ([4]; [5]), the concept of “source” has taken on those forms of communication
that characterise the «onlife» ([10]; [11]) society.
The digital (and digitised) document will be, tomorrow in the future, the (meta)source ([9]) of
historians, allowing them to write about our present. However, this perspective seems to leave
out analogue or archival documents, which may be regarded as non-existent assets —since what
is not displayed on the Internet does not exist! These documents seem to regain their cultural-
historical role only when traditional historians study and analyse them. Several digitisation
projects of archival documents, however, have allowed digital historians to also make use of
these corpora, which, being increasingly abundant, have enabled —also— different scholars to
consider them as the “Big Data of History” ([15]; [34]; [20]; [35]).
Analogue sources are transformed into images enriched with metadata and digital information
that enables their identification on the web. Their volume is increasingly vast and challenging to
manage without the aid of other ICT tools. Two artificial intelligences (i.e., Transkribus and
ChatGPT) now offer the opportunity to enhance archival and historical workflows. They both
enable the automatic transcription of large quantities of manuscripts and facilitate a more
profound analysis, allowing the extraction of extensive information from texts. Therefore, this
helps reconstruct past events with an extraordinary richness of historical details.
Beyond a purely philological-linguistic approach, in the case of historical analysis, source texts
require a certain degree of normalisation and correction in order to be useable in other
computational contexts because, unlike literary compositions —both in prose and verse— or
homogeneous documents such as companies’ cash registers, banking documentation, or even
medical records from a hospital or medical practice, the archival sources used by historians —
to reconstruct even single event or the activities of a person— are characterised by different
writing styles and languages, as in the case of epistles from a monarch or prince drafted by
various secretaries —hence, the different handwriting, even though the epistles come from the
same sender. In other cases, the writing is uniform precisely because it is personally penned by
the sender, as in the case of the letters of lower-rank nobles or wealthy bourgeois. This
uniformity is also particularly evident when there is a strong personal relationship between the
sender and the consignee, so the letter is not entrusted to external writers (for example, a
secretary). An example of this can be found in the 28 letters that are part of the section under
examination, sent by Prior Michele Maria Paternò to Princess Anna Maria Morso Bonanno1.
This variety in calligraphy prevents an HTR model from producing a consistently accurate
transcription without a training phase that may allow historians to transcribe numerous
manuscripts quickly —which is the purpose of HTR technologies.
Handwritten Text Recognition platforms, such as tranScriptorium ([33]), Transkribus ([18]; [26];
[8]; [24]), and eScriptorium ([21])2, have expanded the prospects for archivists and historians.
Digitalisation has undoubtedly facilitated access for historians to numerous archival documents,
creating the conditions for reconstructing historical events with greater detail beyond the
possibility of working with transcribed texts. Furthermore, artificial intelligence tools have
enabled archivists to develop digitisation projects beyond the simple photographic


    1 State Archive of Catania, Biscari, Corrispondenza, ff. 370-414.
    2 Several articles have analyzed the differences between HTR (Handwritten Text Recognition)
    software and systems, with a particular focus on the two mentioned here, Transkribus and
    eScriptorium ([16]; [23]). Additionally, a report of the study day held at the Bibliothèque nationale de
    France on May 9, 2022, provides an insight into the development of these two platforms ([13]).
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representation of documents, as exemplified by the Stockholm City Archive 3, the Mining Hub
digital archive 4, and the Regesta Pomeraniae Monastica 5.
However, automatic transcription is not the only challenge related to digitisation. The ability to
rapidly obtain the transcribed (digital) version of numerous documents from archival collections
poses a “physiological” limit for researchers in quickly analysing their content. Whilst not
intending to engage in a “quantity over quality” debate in this regard, it is safe to say that
digitalisation severely tests the Droysenian capacity of historians to connect facts through the
use of analogy, imagination, and subjective interpretation ([7]).
Quickly transcribing numerous documents in a digital format —albeit with a margin of error—
generates an uncontrollable mass of data and information that cannot be easily organised and
reviewed; moreover, it does not enable the identification of any patterns.
Historical research —since Herodotus— is based on a close reading methodology and the
analysis of a limited number of documents. Numerous digitisation projects —like the collection
of the Library of Congress with millions of newspaper pages and the Finnish Archives’ court
records dating back to the 19th century— pushed historians to start using machine learning
(deep neural networks, in particular) to organise and examine historical documents.
Consequently, the «Big data of History» is both a problem and an opportunity for researchers: it
provides much more information, though there is no evidence of an effective way to sift through
it. However, ICT seems to yield a solution to the problem. A representative case of the
aforementioned observation is that of the seventeenth-century plague in Venice, which
historians described relying on archival sources that collect data and information from
documents that recorded only a few days of that terrible experience. Instead, with the “Venice
Time Machine” project ([19]; [1]), digitisation has allowed scholars to trace up to three years of
events and incidents, increasing knowledge about the epidemic ([22]).
On the other hand, even HTR tools have their limitations ([39]). The models are being constantly
trained and enhanced. Their use has undoubtedly profoundly impacted the Humanities research
field, particularly Archival Science ([28]). However, it is necessary to consider that the
documentary material processed by these technologies may not be correctly transcribed in its
entirety. Each model has an error range (Character Error Rate) that requires scholars to carry
out a thorough revision (1) in case of online distribution of a digital edition of a complex (large
or small) documentary, an epistolary corpus, or other materials, as well as (2) in case of a detailed
historical or linguistic analysis that requires accurate and normalised texts, regardless of their
potential publication and/or dissemination. From the perspective of mass digitisation, such
corrections cannot be entrusted to an individual scholar, as their capacity would be limited to a
few documents, thereby prolonging the research timeline.
However, nowadays, Computer Science and ICT development companies may have found a
solution in the Artificial Intelligence tool named LLM (Large Language Model), which, due to
its characteristics related to the construction and formulation of texts, could be configured as
the best tool for correcting lengthy texts.
Notably, OpenAI was founded in 2015. Since then, significant funding has enabled the company
to develop InstructGPT ([29]), followed by ChatGPT ([2]; [42]; [17]; [30]; [37]), an innovative


    3 Link: https://stockholm-city-archives.transkribus.eu (last access, 20 Jul 2023).
    4 Link: https://transkribus.eu/r/mining-hub/#/ (last access, 20 Jul 2023).
    5 Link: https://transkribus.eu/r/regestapomeraniae/#/ (last access, 20 Jul 2023).
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AI system capable of engaging in dialogue with humans, thus realising Alan Turing’s vision [40]).
From its early stages of development, GPT has emerged as one of the leading Language Models
(LLMs). However, it is essential to note that regardless of the testing it regularly undergoes, the
AI ChatGPT lacks any semantic understanding. Indeed, any test involving “reasoning” is
inherently flawed a priori.
Unquestionably, GPT is anything but a biological entity. Despite its ability to engage in
conversation —it almost seems like discussing with another sentient being— it is not an
intelligent agent that relies on semantic understanding and logical inferences. ChatGPT is an AI
system constructed upon models that enable it to generate syntactically accurate texts and
statements ([12]). However, like other LLMs, ChatGPT is susceptible to hallucination issues
([32]; [27]) in its outputs.
Therefore, its strength lies in the feature to fix and reformulate the syntax of a text or its parts
([12]) correctly.

       The workflow
Suppose it is true that the term “digitalisation” in archival and historical fields denotes the
process of encoding documents/texts into a machine-readable format. In that case, it is even
truer that most archival documentation digitisation projects do not go beyond the metadata
phase. The historical documentation on the Web is, for the most part, a simple photographic
reproduction accompanied by limited information and lacking a descriptive level that includes
the complete transcription of the photographed text. Fortunately, HTR artificial intelligence
tools have shown much more promising prospects to follow in the digitisation processes,
enabling the creation of digital editions of documentation.
Nowadays, digital historians have at their disposal three different HTR platforms (i.e.,
tranScriptorium, eScriptorium, Transkribus), which aim for the same goal while being
structurally different6.
For instance, the Transkribus AI manages to combine, in the same tool, multiple functions and
process management, ensuring more efficient results in terms of transcription accuracy,
exporting results in different formats, and even enabling the possibility of dissemination and
access to the digital edition.
Two artificial intelligence tools —Transkribus and ChatGPT— underwent a test to prove their
effectiveness, performed on a corpus of letters, constituting the “Correspondence” section of
the Paternò Castello, preserved at the Catania’s State Archive. The documentary collection of
the family is very diverse ([3]). It consists of various documents, making it an essential tool for
scholars interested in the History of Sicily and the relationships between the island authorities


    6 The TranScriptorium and eScriptorium AI tools require computer skills for server configuration,
    installation, and application of transcription models, which some humanists seem to lack at times.
    On the other hand, the AI system Transkribus only requires the profile registration on the official
    website (https://transkribus.eu/), and a few simple steps to install the desktop version on a Personal
    Computer. Additionally, there is no need for specific configurations to use the models (or to access
    online repositories like GitHub), as the transcription process takes place entirely on the AI servers.
    Furthermore, unlike other platforms the Transkribus GUI, has a text editor where the user can,
    according to the segmented lines connected to the image, both produce a coherent transcription of
    each line of text and make necessary corrections to spelling errors; the user can also decipher any
    unrecognized symbols and abbreviations. In fact, they can transcribe their own documents and use a
    suite of tagging tools to create rich transcriptions that could also be part of a digital edition.
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and European monarchies. In particular, the test used the epistolary section, a document that,
although homogeneous in structure and layout, contains a large amount of data (names, places,
dates) that need to be mined and numerous handwriting styles (262 senders). The section
consists of 366 letters, and the acquisition process was performed using a Nikon D610 camera
equipped with an AF-S Nikkor 24-120mm f/4G ED VR lens. The photos were collected in a
database created using Claris FileMaker 19 software. After adding metadata, they were converted
to PDF format in order to be uploaded to the chosen Transkribus server for automatic
transcription, that is, the “Transkribus Italian Handwriting M1” public model, which exhibits a
Character Error Rate (CER) of 12.50% for the “train set”7 and 6.70% for the “validation set”8
(Figura 1). Such percentages ensure an optimal transcription, requiring minimal corrections by
the user, as described in Spina ([39]).




                                                Figura 1

Subsequently, the transcriptions were downloaded in “.txt” format without undergoing any
manual corrections or training aimed at reducing the error rate in order to proceed with an
automatic correction of the entire correspondence, initiating a “chat” 9 session to prompt LLM
ChatGPT so that it could analyse each letter to assess whether this OpenAI tool can correct and
normalise them, in a zero-shot learning ([41]; [38]) scenario —which is the typical condition of
non-computer-savvy historians who wish to utilise technological tools for their research.
The prompt given to ChatGPT was to correct a text without linguistic instructions, historical
references, or training on how to provide expansions of abbreviations ([36]).
The type of analysis required did not deviate from the capabilities of AI, and the corrections
obtained met our expectations, allowing us to assert, without hesitation, that the LLM ChatGPT
can be a helpful tool for correcting historical texts, especially those transcribed automatically.
However, a problem arises regarding the length of the texts that require correction, which
contradicts one of the fundamental principles of the digital era: the ability to correct, analyse,
and process vast amounts of data, be it text or numbers.
At the beginning of the test, we used GPT-3, which could only analyse and correct texts of up
to 1000 characters. In our case, this meant processing one letter at a time. This limitation, on the
one hand, prevents historians from obtaining a correction for the entire body of texts in a single
phase. On the other hand, it does not allow a revision process that takes into account linguistic


    7 Set of examples used to fit the parameters of the model, i.e., the data on which the knowledge in
    the net is based.
    8 Set of examples that provides an unbiased evaluation of a model, used to tune the model’s
    parameters during training.
    9 Despite its name, ChatGPT has not been primarily used as a chatbot.
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and syntactic correlations or any relevant data within the corpus that could contribute to a more
thorough correction. Additionally, if scholars intend to correct multiple texts, they will have to
provide as many prompts as the number of letters required.
The latest update of this AI (in May 2023), as stated by OpenAI, no longer has this limitation (Figura
2, Figura 3, and Figura 4). The company claims that «GPT-4 can handle over 25,000 words of text,
enabling use cases such as creating long-form content, engaging in extended conversations, and
conducting document research and analysis». This statement should allow historians to correct the
entire corpus of letters, which consists of approximately 900,000 characters.
However, once again, the desired outcome cannot be achieved: the character limit in GPT-3 is
still active in its subsequent versions (such as GPT-4), and although there has been a significant
improvement in computational instructions and text analysis capabilities, the epistolography,
which comprises 10,662 words, cannot be corrected.




                                               Figura 2




                                               Figura 3




                                               Figura 4

Although ChatGPT is a powerful language generation model, it has a limitation: it lacks access
to external information and internet browsing. Consequently, it cannot provide accurate or
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updated information on a wide range of topics, and it may struggle to generate responses to
complex or unconventional questions ([6]).
This constraint prevents scholars from analysing texts that contain information that could enrich
the understanding of historical events, as in the case of the letters sent by Michele Maria Paternò
to Anna Morso, wife of Ignazio, the fifth prince of Biscari. These letters cover a period (1781-
1786) marked by various events related to the History of Sicily, Naples, and the Bourbon court
(intrigues, attempts at reforms, management of seismic emergencies such as the reconstruction
of the Catania Cathedral, and aspects of religious life). However, they cannot offer valuable
insights into the History of Sicily in the modern era (Figura 5 and Figura 6).




                                                Figura 5




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                                             Figura 6

As a consequence, ChatGPT cannot perform text-to-web comparisons. Furthermore, like most
LLMs, OpenAI’s GPT has not been explicitly trained for information extraction tasks (e.g.,
Named Entity Recognition and Classification — NERC, and relation extraction) ([31]),
especially concerning historical documents (Figura 7).




                                             Figura 7

The correction, therefore, is necessarily performed on the individual epistles.
ChatGPT first aims for a probability distribution over word or token sequences, enabling it to
predict the next word in a sequence. In practice, however, there is an apparent disconnect
between the training of language models (LLMs) and their intended use for valuable cognitive
work. Although LLMs can generate text, they cannot perform accurate corrections. The text
generated by the AI may align with the original text, somewhat satisfying researchers’
expectations (Figura 8). The statistical architecture of the machine allows it to generate a text
where the distribution of tokens conveys the linguistic sense of the text to be corrected.
However, without specific instructions from the user aimed at a potential “ground truth”, in
many cases, even if the AI generates a text with minor corrections, the “next-token-prediction”
may yield incorrect names or expand abbreviations inaccurately. Conversely, it can generate the
name of a month composed of numbers and letters (8bre, 9mbre, Xmbre). Moreover, owing to
its inherent functionality, the AI might introduce names that are absent in the original text or

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paragraphs and may not faithfully adhere to the original content (as shown in Figura 8 and Figura
9).




                                                Figura 8




                                                Figura 9
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We proceeded by providing instructions to improve the result, prompting to expand the
abbreviations found in the text and correcting those that have been expanded incorrectly
(Reinforcement Learning with Human Feedback — RLHF). Subsequently, the user’s
instructions have successfully enabled the correct expansion of abbreviations in other epistles,
resulting in a normalised text version of the latter and a list of all abbreviations present in the
corpus (Figura 10).




                                             Figura 10

AI enhances numerous fields within the Humanities. While historians can now have the
opportunity to work with much larger volumes of data compared to the Past, archivists can
utilise these computational technologies to improve their workflow. Nowadays, Archival Science
is also a “Networked Science” since our archival heritage is constantly being digitised and
disseminated on the web through websites and digital databases. However, the digitisation
process requires a certain level of dynamism based on the principle of constructing a machine-
readable version of the documents. Nevertheless, before reaching the completeness of archival
information and achieving meticulous and analytical digitisation, archivists have the task of
creating cards that at least provide helpful data for online research, even if it is not possible to
include the complete transcription of the photographed document.
In epistolography, creating these cards involves identifying specific information, such as the
individuals involved (sender, recipient, and other persons), places (origin, destination, and other
locations), dates, and, when possible, events. These pieces of information make the digital
archive more dynamic. Therefore, archivists’ work is meticulous and requires a deep
understanding of the written document. For this reason, although recent studies have highlighted
inaccuracies in the recognition of entities in historical documents ([14]), in our specific case,
ChatGPT was able to identify entities and events with absolute precision, providing details that
have led us to create detailed records of the documents. On that account, with the assistance of
ChatGPT, we have successfully identified relevant information to compile records on the
“Biscari Epistolography” website (Figura 11), demonstrating that GPT-3 is a technology that
can genuinely serve archivists and historians.




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                                               Figura 11



       Observations

Historical scholarship is primarily based on text-based analysis —where the original wording of
historical sources often provides new insights and inferences of specific meanings— while
Archival Science is called to reassess the principle of disseminating the documentary heritage.
Thus, both historians and archivists, albeit from different points of view, need to work on texts
that have been transcribed correctly due to the advantages offered by computing normalised text
corpora in modern languages. Firstly, this enables the dissemination of results without the need
for manual normalisation; secondly, it allows for the application of computational linguistic
analysis tools that can uncover additional data, a feature that appears to be crucial for accurate
storytelling. Tools like Keyphrase Digger ([25]) are more effective in processing modern-
language normalised texts than those written in 18th-century style (as seen in Figura 12).


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                                            Figura 12

The unique features of 18th-century texts, such as abbreviations, syntactic errors, and incorrect
word forms, would require specific modelling to train artificial Intelligence. It should be noted
that querying neural networks and the semantic web with an 18th-century text might lead to its
exclusion.
Artificial Intelligence is a rapidly advancing field within Computer Science, focused on creating
intelligent machines capable of human-like thinking and action. While AI has found applications
in various domains, History remains a discipline rooted in archival sources. Accurate digitisation
and automatic transcription projects are essential for providing the necessary information to
develop models that can effectively assist historians in their research.
However, specific observations seem necessary. The digital turn and digitisation projects have
inundated historians with vast historical data extracted from digitised sources, posing challenges
in managing and analysing such Big Data from the Past. ChatGPT —despite lacking sentient
intelligence— utilises semantic processing and syntactic rules to generate a dialogue that may
give the impression of understanding. Although it lacks a biological brain structure, the training
on syntactic structures and instructions makes GPT-3 a valuable tool for normalising historical
source corpora. Corrections made by the AI, even if unrelated to the text itself, such as
reformulating propositions or merging names and abbreviations, do not distort the source but
rather unearth new data for historians to explore.
Historical research relies on archival sources, which transmit more than just the linguistic
structure of the text; they possess semantic structures. Nevertheless, this does not imply that
historical methodology should forego the advantages offered by ITC innovations that ensure
accuracy at specific stages of the workflow of historians. Transkribus, for instance, is an AI-
powered tool used to transcribe digitised archival documents with impressive speed, generating
machine-readable files that enable historians to analyse, process, and create digital editions.
Though ChatGPT cannot invent new historical events, it has been trained to write about History,
albeit not at the same level as scholars. It can be a valuable tool for archivists and historians,
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       S. Spina – Artificial Intelligence in archival and historical scholarship workflow: HTS an ChatGPT


serving their objective and compelling narrative by normalising information from digitised,
encoded, and automatically transcribed sources.



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