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<p>Presents a feature recognition network for pattern recognition that learns the patterns by remembering their different segments. The base algorithm for this network is a Boolean net algorithm that the authors developed during past research. Simulation results show that the network can recognize patterns after significant noise, deformation, translation and even scaling. The network is compared to existing popular networks used for the same purpose, especially the Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval.</p>
character recognition; pattern recognition; neural nets; feature recognition neural network; character recognition; Boolean net algorithm; noise; deformation; translation; scaling; Neocognitron; interconnection complexity; information storage/retrieval

M. Kabuka and B. Hussain, "A Novel Feature Recognition Neural Network and its Application to Character Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 98-106, 1994.
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