A parallel neuromorphic text recognition system and its implementation on a heterogeneous high-performance computing cluster

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.