2026-08-04T13:03:19Z https://dcpapers.dublincore.org/oai

oai:dcpapers.dublincore.org:952109189 2008-09-10 dc-2008

Automatic Metadata Extraction From Museum Specimen Labels Heidorn, P. Bryan Wei, Qin automatic metadata extraction machine learning Hidden Markov Model Naïve Bayes Darwin Core This paper describes the information properties of museum specimen labels and machine learning tools to automatically extract Darwin Core (DwC) and other metadata from these labels processed through Optical Character Recognition (OCR). The DwC is a metadata profile describing the core set of access points for search and retrieval of natural history collections and observation databases. Using the HERBIS Learning System (HLS) we extract 74 independent elements from these labels. The automated text extraction tools are provided as a web service so that users can reference digital images of specimens and receive back an extended Darwin Core XML representation of the content of the label. This automated extraction task is made more difficult by the high variability of museum label formats, OCR errors and the open class nature of some elements. In this paper we introduce our overall system architecture, and variability robust solutions including, the application of Hidden Markov and Naïve Bayes machine learning models, data cleaning, use of field element identifiers, and specialist learning models. The techniques developed here could be adapted to any metadata extraction situation with noisy text and weakly ordered elements. Dublin Core Metadata Initiative 2008-09-10 Text application/pdf text/html https://doi.org/10.23106/dcmi.952109189 https://dcpapers.dublincore.org/article/952109189 Dublin Core Metadata Initiative Conference Proceedings en https://www.wikidata.org/wiki/Q68278022 https://creativecommons.org/licenses/by/4.0/