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Issue No. 05 - May (2013 vol. 62)
ISSN: 0018-9340
pp: 886-899
Robinson E. Pino , Air Force Research Lab, Rome
Richard W. Linderman , Air Force Research Lab, Rome
Morgan Bishop , Air Force Research Laboratory, Rome
Qing Wu , Air Force Research Laboratory, Rome
Qinru Qiu , Syracuse University, Syracuse
ABSTRACT
Given the recent progress in the evolution of high-performance computing (HPC) technologies, the research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition system (ITRS) that serves as the physical layer of machine reading. A parallel computing architecture is adopted that incorporates the HPC technologies with advances in neuromorphic computing models. The algorithm learns from what has been read and, based on the obtained knowledge, it forms anticipations of the word and sentence level context. The information processing flow of the ITRS imitates the function of the neocortex system. It incorporates large number of simple pattern detection modules with advanced information association layer to achieve perception and recognition. Such architecture provides robust performance to images with large noise. The implemented ITRS software is able to process about 16 to 20 scanned pages per second on the 500 trillion floating point operations per second (TFLOPS) Air Force Research Laboratory (AFRL)/Information Directorate (RI) Condor HPC after performance optimization.
INDEX TERMS
Optical character recognition software, Neurons, Computational modeling, Brain models, Computer architecture, Biological neural networks, natural language interfaces, Optical character recognition software, Neurons, Computational modeling, Brain models, Computer architecture, Biological neural networks, machine learning, Heterogeneous (hybrid) systems, distributed architecture
CITATION
Robinson E. Pino, Richard W. Linderman, Morgan Bishop, Qing Wu, Qinru Qiu, "A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster", IEEE Transactions on Computers, vol. 62, no. , pp. 886-899, May 2013, doi:10.1109/TC.2012.50
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