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Unlocking colonial records with Artificial Intelligence. Achieving the automated transcription of large-scale 16th and 17th-century Latin American historical collections

Bibliographic Data

ID19553750
AuthorsPatricia Murrieta Flores (0000-0001-9904-0288, Lancaster University, corresponding author), Rodrigo Vega-Sánchez, Rodrigo Vega‐Sánchez (0000-0002-2405-9045, Lancaster University), Alexander Sánchez-Diaz (University of Alicante), Héctor Francisco Cruz-Ríos (Ciudad de México)
Year2025
Volume11
Issue1
Publication date2025-12-31
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSTAR Science & Technology of Archaeological Research (JOURNAL)
Journal identifiersISSN: 2054-8923 • E-ISSN: 2054-8923
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/20548923.2025.2484828
OpenAlexW4408959748
LanguageEN
References cited15

Between the 16th and 18th centuries, Spanish and Indigenous people produced millions of colonial documents in diverse calligraphic styles in Latin American territories. Accessing their rich information requires specialised palaeographic skills and often indigenous language expertise. Recent advancements in Machine Learning offer promising solutions to these challenges. This study developed two computational methods: (1) a historical document classifier using Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), and (2) Handwritten Text Recognition (HTR) models trained on 16th- and 17th-century Spanish manuscripts via Transkribus. The classifier achieved high accuracy, with F1 scores over 90% for most calligraphic styles, while HTR models produced competitive Character Error Rates, notably 5.25% for Redonda, 8.92% for Itálica Cursiva and 14.15% Procesal Simple. These automated tools are now allowing us to transform previously “unreadable” archives into accessible data. This means that soon, Latin American archives and libraries will be able to unlock centuries of historical information

Archaeology · Cartography · Colonialism · Geography · Latin Americans · Linguistics · Handwritten Text Recognition Techniques · History · Image Processing and 3D Reconstruction · Natural Language Processing Techniques

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Citation velocityhistorical
Highly citedNo

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