A NOVEL FEATURE RECOGNITION NEURAL-NETWORK AND ITS APPLICATION TO CHARACTER-RECOGNITION

This paper 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 we developed during our 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 Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval.