TY - GEN
T1 - Unified perception-prediction model for context aware text recognition on a heterogeneous many-core platform
AU - Qiu, Qinru
AU - Wu, Qing
AU - Linderman, Richard
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80054772226&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054772226&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2011.6033431
DO - 10.1109/IJCNN.2011.6033431
M3 - Conference contribution
AN - SCOPUS:80054772226
SN - 9781457710865
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 1714
EP - 1721
BT - 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011
Y2 - 31 July 2011 through 5 August 2011
ER -