Difference between revisions of "Hackathon Challenge"

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One of the most significant areas of interest for improving the utilization of OCR output is parsing. ''Digitization'' and ''data curation'' of biodiversity museum collections specimens can be sped up if the output from OCR can be parsed faster and more accurately and packaged into semantically meaningful units for insertion into a database.
 
One of the most significant areas of interest for improving the utilization of OCR output is parsing. ''Digitization'' and ''data curation'' of biodiversity museum collections specimens can be sped up if the output from OCR can be parsed faster and more accurately and packaged into semantically meaningful units for insertion into a database.
  

Revision as of 00:18, 11 January 2013

The 2013 AOCR Challenge


The Challenge

One of the most significant areas of interest for improving the utilization of OCR output is parsing. Digitization and data curation of biodiversity museum collections specimens can be sped up if the output from OCR can be parsed faster and more accurately and packaged into semantically meaningful units for insertion into a database.

the specific task

Given a set of images, parse existing OCR output or repeat the OCR with the software of choice and then parse the new OCR output attempting to successfully populate as many of the selected Darwin Core (and other) data elements as possible.

Parsers should produce at least CSV format output where the column headers are Darwin core (http://rs.tdwg.org/dwc/terms/) elements with some extended element names. The full set of valid categories is defined in a definition document in the parsing directory of the A-OCR virtual machine. All of this information needs to be classified on the label so that it can be imported to a database and shared with others over the Internet. The input to the parsing process is OCR text. For the hackathon there will be at least 600 examples of OCR text, in 3 groups of 200, that have been previously properly classified/parsed by humans. This parsed text may be used for training some learning algorithms. This set will also be used for evaluation of performance of parsing algorithms. Overfitting is a potential problem so at the time of the hackathon we may provide additional testing records for evaluation.

There are several potential types of input to the parsing algorithms. The most basic form of input is OCR text in UTF-8 format from multiple engines. There may optionally be OCR with exact spatial information about the location of characters on the original image. This will allow some algorithms to exploit spatial information to identify elements. This format is, however, not a main focus for this hackathon. Also, those wishing to pursue other goals such as image segmentation, finding specific elements, or improving usability & user interfaces to the OCR and parsing tools are encouraged to do so and report back to the group at the hackathon.

Back to the Hackathon Wiki