Unified perception-prediction model for context aware text recognition on a heterogeneous many-core platform

Qinru Qiu, Qing Wu, Richard Linderman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Existing optical character recognition (OCR) software tools can perform text image detection and pattern recognition with fairly high accuracy, however their performance will be significantly impaired when the image of the character is partially blocked or smudged. Such missing information does not hinder the human perception because we predict the missing part based on the word level and sentence level context of the character. In order to mimic the human cognitive behavior, we developed a hybrid cognitive architecture combining two neuromorphic computing models, i.e. brain-state-in-a-box (BSB) and cogent confabulation, to achieve context-aware text recognition. The BSB model performs the character recognition from input image while the confabulation models perform the context-aware prediction based on the word and sentence knowledge bases. The software tool is implemented on an 1824-core computing cluster. Its accuracy and performance are analyzed in the paper.

Original languageEnglish (US)
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages1714-1721
Number of pages8
DOIs
StatePublished - Oct 24 2011
Externally publishedYes
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: Jul 31 2011Aug 5 2011

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2011 International Joint Conference on Neural Network, IJCNN 2011
CountryUnited States
CitySan Jose, CA
Period7/31/118/5/11

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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