Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC)
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López Baldomero, Ana Belén; Nieves Gómez, Juan Luis; Moronta-Montero, Francisco; Martínez Domingo, Miguel Ángel; Fernández-Gualda, Ramón; Hernández Andrés, Javier; Reichert, Anna S.; López Montes, Ana María; Espejo Arias, María Teresa; Romero Mora, Francisco Javier; Valero Benito, Eva MaríaEditorial
Nature
Materia
hyperspectral imaging spectral database historical documents mock-ups Hyperdoc
Fecha
2025-07-16Referencia bibliográfica
López-Baldomero, Ana Belén, et al. "Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC)." Scientific Data 12.1 (2025): 1248.
Patrocinador
This work was supported by Grant PID2021-124446NB-I00 funded by MICIU/AEI /10.13039/501100011033 and by ERDF, EU; Ministry of Universities (Spain) [grant number FPU2020-05532]; and “ESF Investing in your future” [grant number PRE2022-101352]. We would like to acknowledge David Torres Ibáñez, Director of the Archive of the Royal Chancellery of Granada, and Eva Martín López, Director of the Provincial Historical Archive of Granada, for their assistance.Resumen
HYPERDOC is a hyperspectral imaging dataset of historical documents and mock-ups, designed to facilitate research in material identification in the cultural heritage domain. It contains mock-ups of historical inks (metallo-gallate, sepia, carbon-based, and mixtures) on various supports, including some artificially aged, and historical documents from the 15th to 17th centuries (manuscripts, illuminated manuscripts, and family trees). Hyperspectral reflectance images were acquired using line-scan cameras in the VNIR (400-1000 nm) and SWIR (900-1700 nm) ranges and were spatially registered. Small regions of interest, referred to as ‘minicubes’, were extracted from the full document images, and pixel-level ground truth material annotations were performed. False-color RGB images and metadata were included in both the full document and minicube captures. The HYPERDOC dataset has been successfully applied in various experimental studies, including ink classification using machine learning models, spectral unmixing, colorimetric analysis, and binarization. These applications highlight the dataset’s potential, which is publicly available to promote interdisciplinary collaboration and advance the use of hyperspectral imaging in the conservation field.





