From handwritten manuscripts to linked data

Abstract

Museums, archives and digital libraries make increasing use of Semantic Web technologies to enrich and publish their collection items. The contents of those items, however, are not often enriched in the same way. Extracting named entities within historical manuscripts and disclosing the relationships between them would facilitate cultural heritage research, but it is a labour-intensive and time-consuming process, particularly for handwritten documents. It requires either automated handwriting recognition techniques, or manual annotation by domain experts before the content can be semantically structured. Different workflows have been proposed to address this problem, involving full-text transcription and named entity extraction, with results ranging from unstructured files to semantically annotated knowledge bases. Here, we detail these workflows and describe the approach we have taken to disclose historical biodiversity data, which enables the direct labelling and semantic annotation of document images in hand-written archives.