SCHEDULE: NOV 10-16, 2012
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Digitization and Search: A Non-Traditional Use of HPC
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Liana Diesendruck, Luigi Marini, Rob Kooper, Mayank Kejriwal, Kenton McHenry
ROOM:East Entrance
ABSTRACT:
We describe our efforts to provide a form of automated search of handwritten content for digitized document archives. To carry out the search we use a computer vision technique called word spotting. A form of content based image retrieval, it avoids the still difficult task of directly recognizing text by allowing a user to search using a query image containing handwritten text and ranking a database of images in terms of those that contain more similar looking content. In order to make this search capability available on an archive three computationally expensive pre-processing steps are required. We augment this automated portion of the process with a passive crowd sourcing element that mines queries from the systems users in order to then improve the results of future queries. We benchmark the proposed framework on 1930s Census data, a collection of roughly 3.6 million forms and 7 billion individual units of information.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Liana Diesendruck - University of Illinois at Urbana-Champaign
Luigi Marini - University of Illinois at Urbana-Champaign
Rob Kooper - University of Illinois at Urbana-Champaign
Mayank Kejriwal - University of Illinois at Urbana-Champaign
Kenton McHenry - University of Illinois at Urbana-Champaign
Click here to download .ics calendar file
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Digitization and Search: A Non-Traditional Use of HPC
SESSION: Research Poster Reception
EVENT TYPE:
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Liana Diesendruck, Luigi Marini, Rob Kooper, Mayank Kejriwal, Kenton McHenry
ROOM:East Entrance
ABSTRACT:
We describe our efforts to provide a form of automated search of handwritten content for digitized document archives. To carry out the search we use a computer vision technique called word spotting. A form of content based image retrieval, it avoids the still difficult task of directly recognizing text by allowing a user to search using a query image containing handwritten text and ranking a database of images in terms of those that contain more similar looking content. In order to make this search capability available on an archive three computationally expensive pre-processing steps are required. We augment this automated portion of the process with a passive crowd sourcing element that mines queries from the systems users in order to then improve the results of future queries. We benchmark the proposed framework on 1930s Census data, a collection of roughly 3.6 million forms and 7 billion individual units of information.
Chair/Author Details:
Torsten Hoefler (Chair) - ETH Zurich
Liana Diesendruck - University of Illinois at Urbana-Champaign
Luigi Marini - University of Illinois at Urbana-Champaign
Rob Kooper - University of Illinois at Urbana-Champaign
Mayank Kejriwal - University of Illinois at Urbana-Champaign
Kenton McHenry - University of Illinois at Urbana-Champaign
Click here to download .ics calendar file
