Use of semantic segmentation for increasing the throughput of digitisation workflows for natural history collections

Nieva De La Hidalga, Abraham ORCID: https://orcid.org/0000-0001-7348-7612, Owen, David ORCID: https://orcid.org/0000-0002-4028-0591, Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885, Rosin, Paul ORCID: https://orcid.org/0000-0002-4965-3884 and Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 2019. Use of semantic segmentation for increasing the throughput of digitisation workflows for natural history collections. Presented at: Biodiversity_Next 2019, Leiden, The Netherlands, 21-25 October 2019. Biodiversity Information Science and Standards. , vol.3 (e37161) Pensoft, 10.3897/biss.3.37161

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Abstract

The need to increase global accessibility to specimens while preserving the physical specimens by reducing their handling motivates digitisation. Digitisation of natural history collections has evolved from recording of specimens’ catalogue data to including digital images and 3D models of specimens. The sheer size of the collections requires developing high throughput digitisation workflows, as well as novel acquisition systems, image standardisation, curation, preservation, and publishing. For instance, herbarium sheet digitisation workflows (and fast digitisation stations) can digitise up to 6,000 specimens per day; operating digitisation stations in parallel can increase that capacity. However, other activities of digitisation workflows still rely on manual processes which throttle the speed with which images can be published. Image quality control and information extraction from images can benefit from greater automation.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Chemistry Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QH Natural history
Uncontrolled Keywords:	semantic segmentation, image quality management, optical character recognition, digitisation, natural history collections, digital specimens
Additional Information:	Conference Abstract
Publisher:	Pensoft
Funders:	Horizon 2020 Framework Programme of the European Union, H2020-INFRADEV-2016-2017 Grant Agreement No. 777483
Date of First Compliant Deposit:	2 August 2019
Date of Acceptance:	11 June 2019
Last Modified:	27 Apr 2024 01:05
URI:	https://orca.cardiff.ac.uk/id/eprint/124628

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