@article {373,
	title = {iDigBio Augmenting OCR Workshop: OCR and SALIX Parsing},
	year = {2012},
	abstract = {Lafferty, Daryl. Arizona State University Herbarium, SALIX Developer. OCR and SALIX Parsing.},
	keywords = {iDigBio Augmenting OCR Workshop},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-wgw/Laffetry_Gainesville.ppt},
	author = {Daryl Lafferty}
}
@article {568,
	title = {iDigBio Hackathon: SALIX 2},
	year = {2013},
	abstract = {SALIX  is {\textquotedblleft}Semi-Automatic Label Information eXtraction{\textquotedblright} parsing system, developed and used extensively at Arizona State University. The purpose is to parse OCR{\textquoteright}d label data into the respective data fields (e.g. Collector, collection number, etc.). The original SALIX required user intervention with each label to format and proofread. SALIX 2 tries to remove the {\textquotedblleft}Semi{\textquotedblright} and make it fully automatic. Written in C++ in Windows. Development was focused on Lichen labels.
},
	keywords = {C++, collaboration, machine language, Natural Language Processing, optical character recognition, Outreach, parsing algorithms, SALIX, SALIX2},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/SALIX2.ppt},
	author = {Daryl Lafferty}
}
@article {760,
	title = {Poster: Improving the Character of Optical Recognition (OCR)},
	year = {2012},
	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 = {education, machine language, Natural Language Processing, NLP, optical character recognition, Outreach, poster},
	url = {https://www.idigbio.org/sites/default/files/poster-files/ocr_dpaulposter.pdf},
	author = {Robert Anglin and Jason Best and Renato Figueiredo and Edward Gilbert and Nathan Gnanasambandam and Stephen Gottschalk and Elspeth Haston and P. Bryan Heidorn and Daryl Lafferty and Peter Lang and Gil Nelson and Deborah L. Paul and William Ulate and Kimberly Watson and Qianjin Zhang}
}
