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

Drinkwater, Robyn; Cubey, Robert; Haston, Elspeth

doi:10.3897/phytokeys.38.7168

Cited by 29 publications

(21 citation statements)

References 15 publications

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“…Optical character recognition may help sorting entries by collector or country (Drinkwater et al, 2014), as might the development of semi-automated imaging pipelines (Tegelberg et al, 2014). Other projects use citizen science approaches to transcribe specimen labels ((Hill et al, 2012); https://www.…”

Section: Box 3 Digitization Challengementioning

confidence: 99%

Using herbaria to study global environmental change

et al. 2018

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During the last centuries, humans have transformed global ecosystems. With their temporal dimension, herbaria provide the otherwise scarce long-term data crucial for tracking ecological and evolutionary changes over this period of intense global change. The sheer size of herbaria, together with their increasing digitization and the possibility of sequencing DNA from the preserved plant material, makes them invaluable resources for understanding ecological and evolutionary species' responses to global environmental change. Following the chronology of global change, we highlight how herbaria can inform about long-term effects on plants of at least four of the main drivers of global change: pollution, habitat change, climate change and invasive species. We summarize how herbarium specimens so far have been used in global change research, discuss future opportunities and challenges posed by the nature of these data, and advocate for an intensified use of these 'windows into the past' for global change research and beyond.

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Section: Box 3 Digitization Challengementioning

confidence: 99%

Using herbaria to study global environmental change

et al. 2018

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“…In particular, our approach involved a two-step process of automated data scraping followed by curation by hand and quality assurance. Overall, we found that OCR was an efficient method for reducing the labor associated with transcribing analog text records (e.g., Drinkwater et al, 2014). Unfortunately, OCR technology does not have absolute accuracy.…”

Section: Discussionmentioning

confidence: 99%

The National Eutrophication Survey: lake characteristics and historical nutrient concentrations

Stachelek

Ford

Kincaid

et al. 2018

Earth Syst. Sci. Data

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Abstract. Historical ecological surveys serve as a baseline and provide context for contemporary research, yet many of these records are not preserved in a way that ensures their long-term usability. The National Eutrophication Survey (NES) database is currently only available as scans of the original reports (PDF files) with no embedded character information. This limits its searchability, machine readability, and the ability of current and future scientists to systematically evaluate its contents. The NES data were collected by the US Environmental Protection Agency between 1972 and 1975 as part of an effort to investigate eutrophication in freshwater lakes and reservoirs. Although several studies have manually transcribed small portions of the database in support of specific studies, there have been no systematic attempts to transcribe and preserve the database in its entirety. Here we use a combination of automated optical character recognition and manual quality assurance procedures to make these data available for analysis. The performance of the optical character recognition protocol was found to be linked to variation in the quality (clarity) of the original documents. For each of the four archival scanned reports, our quality assurance protocol found an error rate between 5.9 and 17 %. The goal of our approach was to strike a balance between efficiency and data quality by combining entry of data by hand with digital transcription technologies. The finished database contains information on the physical characteristics, hydrology, and water quality of about 800 lakes in the contiguous US (Stachelek et al., 2017, https://doi.org/10.5063/F1639MVD). Ultimately, this database could be combined with more recent studies to generate meta-analyses of water quality trends and spatial variation across the continental US.

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“…Considering that the ideal is to digitise collections once, it is critical that the digitisation process does not limit the eventual uses of the images. Potential applications include basic ones, such as determining the identity of the specimen and reading the label details but they might also include automated extraction of character traits using pattern recognition or information extraction from labels and annotations through optical character recognition (Corney et al 2012, Drinkwater et al 2014, Corney et al 2018. More advanced uses of images demand higher quality images and the increased usefulness must be balanced against the additional costs of capture and storage.…”

Section: Introductionmentioning

confidence: 99%

Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management

Hidalga¹,

Rosin²,

Sun³

et al. 2020

BDJ

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Digitisation of natural history collections has evolved from creating databases for the recording of specimens' catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing ‡ ‡ ‡ § § § § § § © Nieva de la Hidalga A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.

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

Cited by 29 publications

References 15 publications

Using herbaria to study global environmental change

Using herbaria to study global environmental change

The National Eutrophication Survey: lake characteristics and historical nutrient concentrations

Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management

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