Handprinted Chinese character recognition via neural networks
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Abstract A new recognition scheme for handprinted Chinese Characters is presented, which is composed of two phases: a stroke recognizing phase in which a handprinted character is matched with a standard character whose code is the closest to that of the handprinted character. The main idea is to use neural networks instead of a digital computer to perform the task of recognition. The character recognizing neural network is based on the neural network model of a content addressable associative memory (CAAM) developed in this paper. In the CAAM model, one thing will remind us of another, and partial information can retrieve the whole. Simulation results demonstrating of the neural network model are presented. The model may be extended so that it will be also useful in understanding some other pattern analysis in biological systems.
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