Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC)

HYPERDOC is a hyperspectral imaging dataset of historical documents and mock-ups, designed to facilitate research in material identification in the cultural heritage domain.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inks (metallo-gallate, sepia, carbon-based, and mixtures) on various supports, including some artificially aged, and historical documents from the 15th to 17th centuries (manuscripts, illuminated manuscripts, and family trees).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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-07-16
DOI
https://doi.org/10.1038/s41597-025-05599-0
Citations
4
Primary Topic
Cultural Heritage Materials Analysis
Type
article
Field-Weighted Citation Impact
33.09

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC)

Eva M. Valero, Ana López‐Montes, Javier Hernández‐Andrés, Teresa Espejo Arias et al.
4 citations
Scientific Data
Cultural Heritage Materials Analysis
33.09
article

Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC)

Eva M. Valero, Ana López‐Montes, Javier Hernández‐Andrés, Teresa Espejo Arias, Miguel Ángel Martínez‐Domingo, Javier Romero, Ana Belén López-Baldomero, Ramón Fernández-Gualda, Francisco Moronta-Montero, Anna Sofía Reichert, Juan L. Nieves
article en
4 citations

Abstract

HYPERDOC is a hyperspectral imaging dataset of historical documents and mock-ups, designed to facilitate research in material identification in the cultural heritage domain.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.

Scientific DataVol. 12(1)
Universidad de Granada (ES)
European Regional Development Fund, Agencia Estatal de Investigación, European Social Fund
Openalex Percentile: Top 0%
Cultural Heritage Materials Analysis
33.09
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.