The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Unlocking colonial records with Artificial Intelligence. Achieving the automated transcription of large-scale 16th and 17th-century Latin American historical collections
Open accessAbstract
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 This study developed two computational methods: (1) a historical document classifier using Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), and (2) Handwritten Text Recognition (HTR) models trained on 16th-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.
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Cited by
Černý, J., Avramov, K., & Pendse, L. R. (2026) · The Electronic Library · doi:10.1108/el-06-2025-0272 · Cited by 4
Should sensitive archival images be shown? The case for applying AI to colonial archives
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Multimodal Retrieval-Augmented AI for Heritage Landscape Analysis
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Metadata
- Authors
- Murrieta-Flores, P., Vega-Sánchez, R., Sánchez-Diaz, A., & Cruz-Ríos, H. F.
- Affiliations
- Tecnológico de Monterrey
- Lancaster University
- Universidad de Alicante
- Ciudad de México
- Venue
- STAR: Science & Technology of Archaeological Research
- Publisher
- Informa UK Limited
- Published
- 2025-03-28
- Volume
- 11
- Issue
- 1
- ISSN
- 2054-8923 (ISSN-L)
- OpenAlex
- W4408959748
- Semantic Scholar
- 79c7063b8e30c43e051b546cb7135b2dfb5b4067
- Cited by
- 7
- References
- 19
- Citations by year
- 2026: 4 · 2025: 3
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- STAR Science & Technology of Archaeological Research (publishedVersion, cc-by-nc)
- RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) (acceptedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Funders
- UKRI (AH/V009559/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Processing and 3D Reconstruction
- Keywords
- automatic transcription, handwriting recognition, colonial records, paleography, calligraphic style, Transkribus, convolutional neural networks, support vector machine, indigenous languages, archival digitization
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