Digitisation programmes in many institutes frequently involve disparate and irregular funding, diverse selection criteria and scope, with different members of staff managing and operating the processes. These factors have influenced the decision at the Royal Botanic Garden Edinburgh to develop an integrated workflow for the digitisation of herbarium specimens which is modular and scalable to enable a single overall workflow to be used for all digitisation projects. This integrated workflow is comprised of three principal elements: a specimen workflow, a data workflow and an image workflow.The specimen workflow is strongly linked to curatorial processes which will impact on the prioritisation, selection and preparation of the specimens. The importance of including a conservation element within the digitisation workflow is highlighted. The data workflow includes the concept of three main categories of collection data: label data, curatorial data and supplementary data. It is shown that each category of data has its own properties which influence the timing of data capture within the workflow. Development of software has been carried out for the rapid capture of curatorial data, and optical character recognition (OCR) software is being used to increase the efficiency of capturing label data and supplementary data. The large number and size of the images has necessitated the inclusion of automated systems within the image workflow.
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.
This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper was written as part of the SYNTHESYS+ project for software development teams and informatics teams working on new software-based approaches to improve mass digitisation of natural history specimens.
Logistically, the data associated with biological collections can be divided into three main categories for digitisation: i) Label Data: the data appearing on the specimen on a label or annotation; ii) Curatorial Data: the data appearing on containers, boxes, cabinets and folders which hold the collections; iii) Supplementary Data: the data held separately from the collections in indices, archives and literature. Each of these categories of data have fundamentally different properties within the digitisation framework which have implications for the data capture process. These properties were assessed in relation to alternative data entry workflows and methodologies to create a more efficient and accurate system of data capture. We see a clear benefit in the prioritisation of curatorial data in the data capture process. These data are often only available at the cabinets, they are in a format suitable for allowing rapid data entry, and they result in an accurate cataloguing of the collections. Finally, the capture of a high resolution digital image enables additional data entry to be separated into multiple sweeps, and optical character recognition (OCR) software can be used to facilitate sorting images for fuller data entry, and giving potential for more automated data entry in the future.
Verification is the process of identifying and accurately naming the plants in the Living Collections. The Royal Botanic Garden Edinburgh (RBGE) has had a well organised system for verifying plants in place for many years but, despite this, only 26% of the Living Collections has been verified. The process is slow and time consuming and is biased towards groups and geographical areas in which Garden staff have a research interest. In the last two years, however, a new, more targeted approach to verification, to run in tandem with the existing system, has been developed that is more timeefficient. With this approach herbarium material is collected for each accession and the whole group is verified in one intensive session. Trial runs have been conducted on Alnus and Acer to great effect and further tests are being conducted on Mahonia and Spiraea.
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