@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}
}
