Scholarlink

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HYPERDOC is a hyperspectral imaging dataset of historical documents and mock-ups, designed to facilitate research in material identification in the cultural heritage domain.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inks (metallo-gallate, sepia, carbon-based, and mixtures) on various supports, including some artificially aged, and historical documents from the 15th to 17th centuries (manuscripts, illuminated manuscripts, and family trees).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

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hyperspectral dataset of historical documents and mock-ups from 400 to 1700 nm (HYPERDOC)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月16日

查看全文
  • Ana Belén López-Baldomero,
  • Juan Luis Nieves,
  • Francisco Moronta-Montero,
  • Miguel Ángel Martínez-Domingo,
  • Ramón Fernández-Gualda,
  • Javier Hernández-Andrés,
  • Anna Sofía Reichert,
  • Ana López-Montes,
  • Teresa Espejo,
  • Javier Romero,
  • Eva María Valero

摘要

HYPERDOC is a hyperspectral imaging dataset of historical documents and mock-ups, designed to facilitate research in material identification in the cultural heritage domain.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inks (metallo-gallate, sepia, carbon-based, and mixtures) on various supports, including some artificially aged, and historical documents from the 15th to 17th centuries (manuscripts, illuminated manuscripts, and family trees).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