Abstract
A neural network-based on-line Chinese character recognition (OLCCR) system is presented. In this paper, a back-propagation neural network model is proposed for solving the pattern-matching problems in OLCCR, instead of those non-neural network-based algorithms. This OLCCR system will enable us to recognize handwritten Chinese characters in real time. Each recognition process includes two main phases: (A) the feature-extraction phase, in which a feature vector based on reference stroke information can be extracted, and (B) the character-matching phase, in which the feature vector is matched with a group of standard Chinese reference character feature vectors. Experimental results show that it takes about 0.25 s to recognize a handwritten character and that a recognition rate of about 91% can be achieved.
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