@conference {547,
	title = {iConference2013: Augmenting optical character recognition (OCR) for improved digitization: Strategies to access scientific data in natural history collections},
	booktitle = {iConference 2013},
	year = {2013},
	month = {02/2013},
	publisher = {iSchools},
	organization = {iSchools},
	address = {Ft. Worth, Texas},
	abstract = {The Augmenting OCR Working Group (A-OCR WG) at Integrated Digitized Biocollections (iDigBio) seeks to improve community OCR strategies and algorithms for faster, better parsing of OCR output derived from valuable data on natural history collection specimen labels. This task is exceedingly difficult because museum labels are often annotated, and vary in content, form and font. Under the National Science Foundation{\textquoteright}s (NSF) Advancing Digitization of Biological Collections (ADBC) program, iDigBio is building a cyberinfrastructure to aggregate quality data from museum specimens housed in collections across the United States for use by researchers, educators, environmentalists and the public. Since March of 2012, the A-OCR WG formed from community consensus to begin its role in this endeavor, defining reachable goals including setting up a hackathon concurrent with iConference 2013. This paper reports on the definition of some key problems identified by the A-OCR WG since these science problems will drive research and cyberinfrastructure development.},
	keywords = {EF-1115210, iConference2013, iDigBio, information analysis, information organization, information retrieval, information services, machine language, Natural Language, OCR, qualitative data analysis, research methods},
	doi = {10.9776/13266},
	url = {https://www.ideals.illinois.edu/bitstream/handle/2142/39427/266.pdf?sequence=4},
	author = {Deborah L Paul and Heidorn, P. Bryan}
}
@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}
}
@conference {546,
	title = {iConference2013: Improving the Character of Optical Character Recognition (OCR): iDigBio Augmenting OCR Working Group Seeks Collaborators and Strategies to Improve OCR Output and Parsing of OCR Output},
	booktitle = {iConference 2013},
	year = {2013},
	month = {02/2013},
	publisher = {iSchools},
	organization = {iSchools},
	address = {Ft. Worth, Texas},
	abstract = {There are an estimated 2 {\textendash} 3 billion museum specimens world {\textendash} wide (OECD 1999, Ari{\~n}o 2010). In an effort to increase the research value of their collections, institutions across the U. S. have been seeking new ways to cost effectively transcribe the label information associated with these specimen collections. Current digitization methods are still relatively slow, labor-intensive, and therefore expensive. New methods, such as optical character recognition (OCR), natural language processing, and human-in-the-loop assisted parsing are being explored to reduce these costs. The National Science Foundation (NSF), through the Advancing Digitization of Biodiversity Collections (ADBC) program, funded Integrated Digitized Biocollections (iDigBio) in 2011 to create a Home Uniting Biodiversity Collections (HUB) cyberinfrastructure to aggregate and collectively integrate specimen data and find ways to digitize specimen data faithfully and faster and disseminate the knowledge of how to achieve this. The iDigBio Augmenting OCR Working Group is part of this national effort. - speed up the overall digitization process, - lower the cost, - improve overall efficiency, - assure digitized data is fit-for-use (NIBA 2010, Chapman 2005), and - provide the resulting digitized data records to researchers more quickly. The iDigBio Augmenting OCR (A-OCR) working group is actively engaged in identifying opportunities for collaboration to leverage OCR tools and technologies that are successful (both within and outside of the biology digitization domain) and disseminate these tools to the public or seek funding for development.},
	keywords = {EF-1115210, iConference2013, iDigBio, information analysis, information organization, information retrieval, information services, machine language, Natural Language, OCR, qualitative data analysis, research methods},
	doi = {10.9776/13493},
	url = {https://www.ideals.illinois.edu/bitstream/handle/2142/42089/493.pdf?sequence=2},
	author = {Robert Anglin and Jason Best and Figueiredo, Renato and Gilbert, Edward and Gnanasambandam, Nathan and Gottschalk, Stephen and Haston, Elspeth and Heidorn, P. Bryan and Lafferty, Daryl and Lang, Peter and Nelson, Gil and Deborah L Paul and Ulate, William and Kimberly Watson and Zhang, Qianjin}
}
@proceedings {548,
	title = {iConference2013: Workshop -- Help iDigBio Reveal Hidden Data: iDigBio Augmenting OCR Working Group Needs You ($\#$CNFWSP11)},
	year = {2013},
	abstract = {Integrated Digitized Biodiversity Collections, iDigBio, is an initiative funded under the National Science Foundation{\textquoteright}s (NSF) Advancing Digitization of Biological Collections (ADBC) program set up to help natural history museums get specimen data for hundreds of millions of specimens out of drawers, off of labels, out of field notebooks, out of old publications and into integrated databases for everyone{\textquoteright}s use. The iDigBio Augmenting OCR Working Group needs your wisdom, knowledge and collaboration as part of our multi-faceted approach to improve OCR strategies and natural language processing (NLP) algorithms used in digitization. Our workshop panelists, members of our working group, are eager to introduce the iSchools community to our challenges and get your input in our break-out sessions. Our research areas of interest include: image segmentation, autocorrection of typographical errors, semantic autocorrection, autonormalization, automated text segmentation, generating consensus records and user interfaces for these tasks. We seek your insights, collective experiences and partnership in order to find ways to improve the digitization process to create a national searchable online specimen-based data set that is fit-for-use by scientists and the public. Some ideas generated in this session may be implemented at the iDigBio hackathon being held at the Botanical Research Institute of Texas (BRIT) during the iConference.
},
	keywords = {aocr, EF-1115210, iConference2013, iDigBio hackathon, information analysis, information organization, information retrieval, information services, iSchools, machine language, Natural Language, OCR, qualitative data analysis, research methods},
	doi = {10.9776/13471},
	url = {http://idigbio.adobeconnect.com/p6463wkhtx1/},
	author = {Paul, Deborah L. and Heidorn, P. Bryan and Best, Jason and Gilbert, Edward and Neill, Amanda and Nelson, Gil and Ulate, William}
}
@conference {1630,
	title = {iConference2013:Help iDigBio reveal hidden data: iDigBio Augmenting OCR working group needs you - Part II},
	year = {2013},
	publisher = {iSchools},
	organization = {iSchools},
	address = {Ft. Worth, Texas},
	abstract = {Integrated Digitized Biocollections (iDigBio) is a nation-wide effort funded by the National Science Foundation (NSF) to digitize data from hundreds of millions of natural history museum specimens. In a concerted five-part outreach effort, the iDigBio Augmenting Optical Character Recognition Working Group (A-OCR WG) coordinated a 2013 iConference Workshop, Poster, Notes submission, Alternative Event and a concurrent Hackathon hosted by the Botanical Research Institute of Texas (BRIT). The Workshop titled, " Help iDigBio Reveal Hidden Data: iDigBio Augmenting OCR Working Group Needs You" introduces the iSchools community to iDigBio and the A-OCR WG mission and challenges to improve digitization efficiency. This related Alternative Event provides the A-OCR WG an opportunity to report back to iConference Workshop attendees about our first experience using a Hackathon model to work on parsing and user interface design issues specific to our needs. We anticipate to a lively, open discussion with event attendees and future collaborators.},
	keywords = {EF-1115210, iConference2013, iDigBio},
	url = {http://hdl.handle.net/2142/42515},
	author = {Paul, Deborah L. and Heidorn, P. Bryan and Jason Best and Edward Gilbert and Amanda Neill and Ulate, William}
}
