@article {235,
	title = {Darwin Core: An Evolving Community-Developed Biodiversity Data Standard},
	journal = {PLoS One},
	volume = {7},
	year = {2012},
	month = {01/2012},
	chapter = {e29715},
	abstract = {Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.},
	keywords = {Darwin Core, Data Standards, metagenomics},
	doi = {10.1371/journal.pone.0029715},
	url = {http://www.plosone.org/article/info\%3Adoi\%2F10.1371\%2Fjournal.pone.0029715},
	author = {John Wieczorek},
	editor = {David Bloom and Robert Guralnick and Stan Blum and Markus D{\"o}ring and Renato Giovanni and Tim Robertson and David Vieglais}
}
@article {556,
	title = {iConference2013: HERBIS/LABELX -- Machine Learning Approach to Parsing OCR Text},
	year = {2013},
	abstract = {A central goal of the digitization of museum specimen labels is to parse the content of the labels into standard fields such as those identified in the Darwin Core (DwC). Museum specimen labels number in the billions and may be hundreds of years old or created recently. They have some minimal layout consistency within subsets of labels but overall have a very high layout variability. The text generated via OCR from these labels tends to have a high rate of errors. This combination makes it extremely difficult for programmers to write rule systems or other matching algorithms to classify the sub-elements of the labels into the DwC fields. Statistical methods such as Naive Bayes, Hidden Markov Models and N-Gramming can be combined with human supervision and authority files to successfully parse OCR output into proper fields and correct some OCR errors based on context. },
	keywords = {Augmenting OCR, Darwin Core, Hidden Markov Models, Human Transcription, Machine Learning, N-Gramming, Naive Bayes, Natural Language, Parsing},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/LABELX.pdf},
	author = {Heidorn, P. Bryan and Zhang, Qianjin}
}
@article {559,
	title = {iConference2013: Introducing iDigBio and the Augmenting OCR Working Group},
	year = {2013},
	abstract = {Highlights of this introduction include: "What is iDigBio? Who is the AOCR wg?" Why we are here at iConference2013? Where did we come from and what makes us unique? Where is our data coming from? and How can the iSchools community get involved?{\textquotedblright} The AOCR wg is working to find ways to speed up and improve access to digitization of natural history museum specimen data and we think the Information Science community can help. Five talks follow, explaining unique key parts of our story. Note the iDigBio* Augmenting Optical Character Recognition Working Group (AOCR wg) put together 4 submissions for iSchools iConference2013: this workshop, a paper, a poster, and an alternative event. All of these are concurrent with a Hackathon at the Botanical Research Institute of Texas (BRIT) as part of a strategic outreach effort. Interested parties are encouraged to participate in our Hackathon, join an existing iDigBio working group, propose and host a workshop, and contribute to our forums and online materials.

        Integrated Digitized Biocollections (iDigBio) is a National Science Foundation (NSF) project funded under the Advancing Digitization of Biological Collections (ADBC) program. Thematic Collection (Museum) Networks (TCNs) are NSF-funded to digitize specimen data needed to answer grand challenge questions and provide that data to iDigBio. iDigBio is building a cyberinfrastructure to integrate data from museums across the USA, making it accessible to everyone. 

},
	keywords = {Augmenting Optical Character Recognition, Darwin Core, iConference2013, iDigBio, Information Science, Natural Language Processing, Parsing, XML},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/LABELX.pdf/iConference2013WorkshopDP.pptx},
	author = {Deborah L Paul}
}
@article {558,
	title = {iConference2013: Symbiota -- Creating an OCR and NLP enabled user interface and workflow to efficiently digitize 2.3 million lichen and bryophyte specimens},
	year = {2013},
	abstract = {This talk focuses on how the Lichen and Bryophyte Climate Change TCN (LBCC) project plans to make use of Optical Character Recognition (OCR) and Natural Language Processing (NLP) to help process over 2.3 million North American lichen and bryophyte specimens held within over 60 U.S. herbaria. A strong component of the LBCC image processing workflow includes separate automated OCR and NLP processing steps that hope to capture a significant component of the metadata found on the specimen labels. After converting specimen images to text fragments using the open source Tesseract OCR engine, previously developed and custom designed NLP algorithms will be used to parse the results into the appropriate Darwin Core fields. The resulting data will be then be imported into a central database and made public through a unified user interface. The final steps will include transferring the results back to the management database systems used by each collection.

        The presentation will begin with a brief overview of the LBCC project along with a simple workflow diagram. The OCR/NLP components of the workflow will be emphasized in greater detail. The Symbiota user interface will be displayed with a short explanation of how it will be used to digitize the label metadata. Current limitations and challenges with OCR and NLP processing will be explained as they pertain to this project. Several solutions to these issues that are being explored by the project will be explained in detail along with their preliminary results. 

},
	keywords = {Augmenting Optical Character Recognition, Darwin Core, Natural Language Processing, User Interface, WorkFlows},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/iSchool.pptx},
	author = {Edward Gilbert}
}
