@article {697,
	title = {Australian Museum Data capture of specimen labels using volunteers},
	year = {2008},
	keywords = {Databasing, LabelCapture, WorkFlows},
	url = {http://australianmuseum.net.au/Uploads/Documents/23183/Data\%20Capture\%20of\%20specimen\%20labels\%20using\%20volunteers\%20-\%20Tann\%20and\%20Flemons\%202008.pdf},
	author = {John Tann and Paul Flemons}
}
@article {706,
	title = {Digital Bee Collection Network : DBCNet ( NSF-BRC Grant )},
	year = {2010},
	keywords = {WorkFlows},
	url = {http://ecnweb.org/sites/default/files/21a_Yanega_2010.pdf},
	author = {Yanega}
}
@article {707,
	title = {Digital Imaging of Biological Type Specimens: A Manual of Best Practice, Results from a study of the European Network for Biodiversity Information},
	year = {2005},
	keywords = {Imaging, LabelCapture, WorkFlows},
	url = {http://imsgbif.gbif.org/CMS_ORC/?doc_id=2429\&download=1},
	author = {Christoph L. H{\"a}user and Axel Steiner and Joachim Holstein and Malcolm J. Scoble}
}
@article {709,
	title = {Digitizing the Yale Collections {\textendash} it takes a Village},
	year = {2010},
	keywords = {Databasing, Imaging, LabelCapture, UseCase, WorkFlows},
	url = {http://ecnweb.org/sites/default/files/gall-ecn-posted.pdf},
	author = {L. Munstermann and L. Gall}
}
@article {712,
	title = {GBIF TRAINING MANUAL 1: DIGITIZATION OF NATURAL HISTORY COLLECTIONS DATA.},
	year = {2008},
	keywords = {Databasing, Imaging, LabelCapture, WorkFlows},
	url = {http://www.infoandina.org/system/files/recursos/GBIF_TM1.pdf}
}
@article {713,
	title = {How to Digitize Large Insect Collections? Preliminary Results of the Dig Project},
	keywords = {WorkFlows},
	url = {http://phthiraptera.info/Publications/9073.pdf},
	author = {D. Lampe and K-H. Striebing}
}
@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}
}
@article {545,
	title = {iDigBio Hackathon: 1st iDigBio - BRIT Hackathon},
	year = {2013},
	abstract = {Overview of the Hackathon goals and introduction to iDigBio for those new to the project. Review this presentation before the other hackathon presentations to learn about the aOCR working group and the talks / presentations / work being done at this hackathon and after.},
	keywords = {Hackathon, Image Segmentation, Machine Learning, Natural Language, OCR, Parsing, Web Services, WorkFlows},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/1stiDigBio_BRITHackathon.pptx},
	author = {Deborah L Paul}
}
@article {714,
	title = {Image Capture and Processing: An Overview, Consortium of Pacific Northwest Herbaria},
	year = {2010},
	publisher = {Consortium of Pacific Northwest Herbaria},
	keywords = {Imaging, LabelCapture, WorkFlows},
	url = {http://www.pnwherbaria.org/documentation/imaging-overview.pdf},
	author = {Ben Legler}
}
@article {236,
	title = {Increasing the efficiency of digitization workflows for herbarium specimens},
	journal = {ZooKeys},
	volume = {209},
	year = {2012},
	month = {07/2012},
	chapter = {103},
	abstract = {The New York Botanical Garden Herbarium has been databasing and imaging its estimated 7.3 million plant specimens for the past 17 years. Due to the size of the collection, we have been selectively digitizing fundable subsets of specimens, making successive passes through the herbarium with each new grant. With this strategy, the average rate for databasing complete records has been 10 specimens per hour. With 1.3 million specimens databased, this effort has taken about 130, 000 hours of staff time. At this rate, to complete the herbarium and digitize the remaining 6 million specimens, another 600, 000 hours would be needed. Given the current biodiversity and economic crises, there is neither the time nor money to complete the collection at this rate.

Through a combination of grants over the last few years, The New York Botanical Garden has been testing new protocols and tactics for increasing the rate of digitization through combinations of data collaboration, field book digitization, partial data entry and imaging, and optical character recognition (OCR) of specimen images. With the launch of the National Science Foundation{\textquoteright}s new Advancing Digitization of Biodiversity Collections program, we hope to move forward with larger, more efficient digitization projects, capturing data from larger portions of the herbarium at a fraction of the cost and time.},
	keywords = {digital imaging, field books, georeferencing, Herbarium specimen digitization, WorkFlows},
	doi = {10.3897/zookeys.209.3125},
	url = {http://www.pensoft.net/journals/zookeys/article/3125/increasing-the-efficiency-of-digitization-workflows-for},
	author = {Melissa Tulig},
	editor = {Nicole Tarnowsky and Michael Bevans and Anthony Kirchgessner and Barbara Thiers}
}
@article {717,
	title = {Innovative workflows for efficient data capture in an entomological collection: The MCZ Lepidoptera Rapid Data Capture Project},
	year = {2010},
	keywords = {Databasing, LabelCapture, UseCase, WorkFlows},
	url = {http://www.ecnweb.org/dev/files/12_Eastwood_2010.pdf},
	author = {P. Morris and R. Eastwood and L. Ford}
}
@article {238,
	title = {Integrating specimen databases and revisionary systematics},
	journal = {ZooKeys},
	volume = {209},
	year = {2012},
	month = {07/2012},
	chapter = {255},
	abstract = {Arguments are presented for the merit of integrating specimen databases into the practice of revisionary systematics. Work flows, data connections, data outputs, and data standardization are enumerated as critical aspects of such integration. Background information is provided on the use of {\textquotedblleft}barcodes{\textquotedblright} as unique specimen identifiers and on methods for efficient data capture. Examples are provided on how to achieve efficient workflows and data standardization, as well as data outputs and data integration.},
	keywords = {EF-1115210, revisionary systematics, specimen databases, WorkFlows},
	doi = {10.3897/zookeys.209.3288},
	url = {http://www.pensoft.net/journals/zookeys/article/3288/integrating-specimen-databases-and-revisionary-systematics},
	author = {Randall T. Schuh}
}
@article {720,
	title = {New York Botanical Garden Virtual Herbarium Best Practices Guide},
	keywords = {Mobilization, Technology, WorkFlows},
	url = {http://sciweb.nybg.org/Science2/hcol/mtsc/NYBG_Best_Practices.doc}
}
@article {727,
	title = {Semi-automated workflows for acquiring specimen data from label images in herbarium collections, Taxon},
	journal = {Taxon},
	volume = {59},
	year = {2010},
	pages = {1830-1842},
	keywords = {Imaging, LabelCapture, WorkFlows},
	url = {http://www.ingentaconnect.com/content/iapt/tax/2010/00000059/00000006/art00014}
}
@article {728,
	title = {Specimen Imaging Documentation: Consortium of Pacific Northwest Herbaria, Version 4.0.},
	year = {2011},
	publisher = {Consortium of Pacific Northwest Herbaria},
	keywords = {Imaging, LabelCapture, WorkFlows},
	url = {http://www.pnwherbaria.org/documentation/imaging-documentation-v4.pdf}
}
@article {729,
	title = {The use of specimen label images for efficient data acquisition in research collections cataloguing: Workflow},
	year = {2006},
	keywords = {WorkFlows},
	url = {http://tdwg2006.tdwg.org/fileadmin/2006meeting/slides/GranzowCerda_ImagesM\%C3\%A9xMichCatalog_abs0098.pdf},
	author = {Ingio Granzow-de la Cerda and Juan Carols Gomez-Martinez and Jose Luis Garcia-Castillo}
}
