Developing integrated workflows for the digitisation of herbarium specimens using a modular and scalable approach

Haston, Elspeth; Cubey, Robert; Pullan, Martin R.; Atkins, H. J.; Harris, David J.

doi:10.3897/zookeys.209.3121

Cited by 31 publications

(36 citation statements)

References 7 publications

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“…The workflows at RBGE have been developed in such a way that they are ‘modular’ (Haston et al 2012b), to allow flexibility in the stages of digitising specimens. All specimen images are passed through ABBYY Recognition Server (Abbyy 2014) which provides the OCR output in the form of a text file.…”

Section: Introductionmentioning

confidence: 99%

The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels

Drinkwater¹,

Cubey²,

Haston³

2014

PHYTOKEYS

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At the Royal Botanic Garden Edinburgh (RBGE) the use of Optical Character Recognition (OCR) to aid the digitisation process has been investigated. This was tested using a herbarium specimen digitisation process with two stages of data entry. Records were initially batch-processed to add data extracted from the OCR text prior to being sorted based on Collector and/or Country. Using images of the specimens, a team of six digitisers then added data to the specimen records. To investigate whether the data from OCR aid the digitisation process, they completed a series of trials which compared the efficiency of data entry between sorted and unsorted batches of specimens. A survey was carried out to explore the opinion of the digitisation staff to the different sorting options. In total 7,200 specimens were processed.When compared to an unsorted, random set of specimens, those which were sorted based on data added from the OCR were quicker to digitise. Of the methods tested here, the most successful in terms of efficiency used a protocol which required entering data into a limited set of fields and where the records were filtered by Collector and Country. The survey and subsequent discussions with the digitisation staff highlighted their preference for working with sorted specimens, in which label layout, locations and handwriting are likely to be similar, and so a familiarity with the Collector or Country is rapidly established.

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Section: Introductionmentioning

confidence: 99%

The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels

Drinkwater¹,

Cubey²,

Haston³

2014

PHYTOKEYS

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“…Given the breadth in database management systems, imaging equipment and configurations, institutional infrastructure, variations in physical facilities, goals for digitization programs, and other differences between institutions, a modular approach facilitates institutional adaptations and encourages the creation of workflows customized to the institution (Haston et al, 2012). Editable and PDF versions of these workflows are also available for download and customization at: github.com/iDigBioWorkflows/PaleontologyDigitizationWorkflows.…”

Section: Digitization Workflowsmentioning

confidence: 99%

“…Currently, optical character recognition (OCR) software is not widely used in paleontological collections, and is not included in this module due to the difficulties associated with handwritten labels and the need to handle specimens to remove the label, which is generally located underneath the specimen. OCR has been successfully implemented in other disciplines where labels are easily visible, such as herbarium sheets (Barber, 2012;Barber et al, 2013;Haston et al, 2012;Lafferty and Landrum, 2009), and may prove effective for some paper records in paleontological collections, such as typed locality cards or catalog ledgers. Additionally, once paper records have been imaged, collections could take advantage of crowdsourcing projects, such as Notes from Nature (www.notesfromnature.org/), which allow citizen scientists to transcribe data.…”

Section: Workflow Modulesmentioning

confidence: 99%

“…The development and documentation of effective workflows is a primary component in the digitization of neontological and paleontological collections (Granzow-de la Cerda and Beach, 2010;Vollmar et al, 2010;Beaman and Cellinese, 2012;Haston et al, 2012;Nelson et al, 2012;Weiss, 2013;Wolniewicz, 2009). Nevertheless, there is no current workflow documentation outlining best or recommended practices for the digitization of paleontological collections.…”

Section: Introductionmentioning

confidence: 99%

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Digitization workflows for paleontology collections

Karim

Burkhalter²,

Farrell

et al. 2016

Palaeontologia Electronica

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The development of digitization workflows is an essential part of any formalized large-scale digitization program. Paleontological collections literature has addressed the need for, and utility of, digitized collections for nearly four decades, but no modern, community-vetted set of digitization workflows to accomplish this goal has been widely adopted. With the advent of the U.S. National Science Foundation's (NSF) Advancing the Digitization of Biodiversity Collections (ADBC) program in 2011, iDigBio, NSF's national coordinating center for facilitating digitization, in collaboration with broad community representation from numerous institutions, launched a series of working groups to address workflow development across all major preparation types. Workflow modules have been developed for pre-digitization curation, data entry, imaging objects (catalogs, field notes and other materials not stored with specimens, labels, two-and three-dimensionally preserved specimens), image processing, and proactive digitization. Modules and the tasks they include may be implemented in any order and customized for specific configurations and institutional parameters. The workflows are made publicly available for download and customization at GitHub and via the iDigBio documentation pages. A review of platforms for electronic data publishing through online aggregators, a crucial step in any digitization program, is also provided.

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“…Optical character recognition (OCR) is a promising method for typewritten material (e.g. Haston et al 2012;Tulig et al 2012). Crowdsourcing (Flemons and Berents 2012;Hill et al 2012;Herbaria United 2014;Les Herbonautes 2014) has lately gained much attention, but is not an appropriate solution for inhouse and time-bound project work.…”

Section: Introductionmentioning

confidence: 99%

DigiWeb - a workflow environment for quality assurance of transcription in digitization of natural history collections

et al. 2014

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-Data produced by digitization increases the scientific use of natural history collections. However, in mass digitization, attention must be paid to the flawless management of the workflows, and high quantities of end results should not be compromised by a low standard of quality. A web-based environment DigiWeb was created for controlling the workflow of transcribing data from images of natural history specimens. Using DigiWeb, it was possible to manage the workflow of transcription and data proofing, include all participants to the workflow, allow collaboration and training, and also to provide useful processing features. The data emerging from this process pass quality control standards which are supported by DigiWeb and based on the strict requirements of the ISO 2859 standard.

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Developing integrated workflows for the digitisation of herbarium specimens using a modular and scalable approach

Cited by 31 publications

References 7 publications

The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels

The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels

Digitization workflows for paleontology collections

DigiWeb - a workflow environment for quality assurance of transcription in digitization of natural history collections

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