@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 {569,
	title = {iConference2013: The Apiary Project: a workflow for text extraction and parsing for herbarium specimens},
	year = {2013},
	abstract = {The Apiary Project: combining OCR technology, OCR output from herbarium specimen or other images containing museum specimen data, well-developed regular expressions for parsing output to Darwin Core fields, and humans-in-the-digitization-loop for an elegant, sophisticated user-interface for a workflow designed to maximize the value of the human interaction, minimize steps, and speed data throughput.},
	keywords = {Augmenting Optical Character Recognition, collaboration, EF-1115210, Hackathon, iConference2013, Machine Learning, Natural Language Processing, parsing algorithms, Research, Transcription, workflow},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/aocr-hackathon/JBest-Apiary-iConference.ppt},
	author = {Jason Best}
}
@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 {1328,
	title = {SPNHC 2014: Progress in Digitization: Using optical character recognition (OCR) output in digitization},
	year = {2014},
	abstract = {Before iDigBio, that{\textquoteright}s Integrated Digitized Biocollections, others began the work of figuring out how to use OCR output with machine learning (ML) and natural language processing (NLP) to improve the efficiency and speed with which data from museum specimen label images can be captured and validated. The Augmenting Optical Character Recognition (aOCR WG) Working Group at iDigBio is pleased to be building on that foundation. Improvements have been realized in parsing algorithms and visualization of data. Recently, researchers at The Royal Botanic Garden Edinburgh (RBGE) successfully used word clouds from OCR output to reveal useful data, otherwise dark, until a specimen label was digitized completely (Haston, et al TDWG 2013). Their work indicates this method results in greater transcriber job satisfaction. Inspired by this work, the OCR Integration Track Team at the iDigBio Citscribe Hackathon showed how indexing, scoring, and visualizing OCR output reveals otherwise hidden search terms, uncovers errors, and can improve the data transcriber and data validator experience. Using open-source software, we presented these ideas to those with transcription tools up-and-running including Notes From Nature, Biodiversity Heritage Library (BHL), ALA Biodiversity Volunteer Portal, Smithsonian Digital Volunteers, and the Lichen, Bryophytes and Climate Change Volunteer Portal using Symbiota software. The aOCR WG is working collaboratively with the Joint Research Activity (JRA) Synthesys3 Project to share expertise for automated data collection for digital images. Got text in your images? How might OCR output work for you? Come talk to our aOCR WG to find out and to share your expertise.},
	keywords = {confidence scores, data visualization, digitization, Machine Learning, Natural Language Processing, optical character recognition, SPNHC 2014, SPNHC 2014: Progress in Digitization},
	url = {https://www.idigbio.org/sites/default/files/workshop-presentations/spnhc2014/Paul_UsingOCRoutput.pptx},
	author = {Deborah L Paul and Andrea Matsunaga and Miao Chen and Jason Best and Sylvia Orli and Elspeth M Haston}
}
