2013 AOCR Hackathon Wiki: Difference between revisions
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=== Overview of the Challenge === | === Overview of the Challenge === | ||
*2013 iDigBio AOCR [[Hackathon Challenge]] | *2013 iDigBio AOCR [[Hackathon Challenge]] | ||
** overall description of the problem | ** overall description of [[Hackathon Challenge#the_problem| the problem]] | ||
** the specific challenge: parse OCR output to find values for these [[2013 hackathon data elements| "core" fields]] | ** the specific challenge: parse OCR output to find values for these [[Hackathon Challenge#core_data_elements| core data elements]] | ||
** | [[2013 hackathon data elements| "core" fields]] | ||
** read about [[Hackathon Challenge#the_metrics| the_metrics]] to be used | |||
link to explanations and examples of the 3 data sets | link to explanations and examples of the 3 data sets | ||
Revision as of 22:20, 10 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 problem
- the specific challenge: parse OCR output to find values for these core data elements
"core" fields
- read about the_metrics to be used
link to explanations and examples of the 3 data sets
set 1: LBCC label images set 2: NYBG and BRIT label images set 3: CalBug ENT label images
link to page summarizing the rules we followed to transcribe the gold set (and others)
link to a page listing known "specific" issues / challenges
how to get OCR to ignore a map (reduce OCR confusion)
... and ___ present a challenge and confuse OCR and parsing.
figure out an algorithm that would separate images into sets with
no handwriting, little handwriting (mostly text typed or printed), lots of handwriting
link to user interface wish list
link labelx to apiary and symbiota what else?
*Thank you Nesent, Hilmar Lapp and the HIP working group for this model.