2013 AOCR Hackathon Wiki: Difference between revisions
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*2013 iDigBio AOCR [[Hackathon Challenge]] | *2013 iDigBio AOCR [[Hackathon Challenge]] | ||
**overall description of [[Hackathon Challenge#the_challenge| The Challenge]] | **overall description of [[Hackathon Challenge#the_challenge| The Challenge]] | ||
**[[Hackathon Challenge#the_specific_task| The Specific Task]]: parse OCR output to find values for these [ | **[[Hackathon Challenge#the_specific_task| The Specific Task]]: parse OCR output to find values for these [2013_hackathon_data_elements] | ||
**[[Hackathon Challenge#metrics_and_evaluation| Metrics and Evaluation]] to be used | **[[Hackathon Challenge#metrics_and_evaluation| Metrics and Evaluation]] to be used | ||
**[[Hackathon Challenge#three_data_sets| Three Data Sets]] | **[[Hackathon Challenge#three_data_sets| Three Data Sets]] | ||
Revision as of 14:20, 11 January 2013
Welcome to the 2013 iDigBio AOCR Hackathon Wiki
- Short URL to this hackathon wiki http://tinyurl.com/aocrhackathonwiki
- Those participating in the first iDigBio AOCR Hackathon need an iDigBio account.
- Note: This wiki page undergoing frequent updates and some participants have wiki edit permissions and will add to / update / edit these pages before, during and after the hackathon.
Links to Logistics, Communication, and Participant Information
- Participant List
- Call for Participation
- Application Form
- Travel, Food, Lodging, Connectivity Logistics
- 2013 Hackathon Listserv, a mailing list for Hackathon Participants at aocr-hackathon-l@lists.ufl.edu
Overview of the Challenge
- 2013 iDigBio AOCR Hackathon Challenge
- overall description of The Challenge
- The Specific Task: parse OCR output to find values for these [2013_hackathon_data_elements]
- Metrics and Evaluation to be used
- Three Data Sets
- There are three data sets, that is, three different sets of images of museum specimen labels. Participants, working alone or in groups, may work on one or more data sets as they choose. The sets have been ranked, easy, medium, hard, as an estimate of how difficult it might be to successfully get good parsed data from the OCR output from each data set.
- Accessing the Data
"core" fields
Frequently Asked Questions
Choosing Images and Parsing Decisions
Issues that need work
- Known OCR, ML, NLP Issues and challenges
- Human-in-the-loop: User Interface Wish List
*Thank you NESCent, Hilmar Lapp and the HIP working group for this model.