2013 AOCR Hackathon Wiki: Difference between revisions
Jump to navigation
Jump to search
Line 6: | Line 6: | ||
=== Logistics and Participant Information === | === Logistics and Participant Information === | ||
2013 Hackathon [[2013 Hackathon Participants| Participant List]] | 2013 Hackathon [[2013 Hackathon Participants| Participant List]]<br> | ||
2013 Hackathon [http://tinyurl.com/aocrHack Call for Participation] | 2013 Hackathon [http://tinyurl.com/aocrHack Call for Participation]<br> | ||
2013 Hackathon [http://tinyurl.com/idigbioAOCRHackathon Application Form]* | 2013 Hackathon [http://tinyurl.com/idigbioAOCRHackathon Application Form]*<br> | ||
2013 Hackathon [[2013 Hackathon Logistics| Travel, Food, Lodging, Connectivity Logistics]] | 2013 Hackathon [[2013 Hackathon Logistics| Travel, Food, Lodging, Connectivity Logistics]] | ||
Revision as of 18:20, 10 January 2013
Welcome to the 2013 iDigBio AOCR Hackathon Wiki
- Short URL to this hackathon wiki http://tinyurl.com/aocrhackathonwiki
- Note: This wiki page undergoing frequent updates.
- Those participating in the first iDigBio AOCR Hackathon need an iDigBio account.
- Some participants have wiki edit permissions and will add to / update / edit these pages before, during and after the hackathon.
Logistics and Participant Information
2013 Hackathon Participant List
2013 Hackathon Call for Participation
2013 Hackathon Application Form*
2013 Hackathon Travel, Food, Lodging, Connectivity Logistics
link to page/s describing the problem, the specific challenge and metrics to be used
parse to find values for these "core" fields
link to explanations 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 data examples
Mailing List for Hackathon aocr-hackathon-l@lists.ufl.edu
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.