The perception of a letter in the context of a word is easier than in the context of a random letter sequence. It appears that our knowledge about words can influence our perception process. McClelland and Rumelhart (1981) propose an interactive activation model to account for the interaction between our knowledge about words and our visual input. They use their model to explain how these interactions facilitate perception. In their account, word context effect is a constant independent of the identity of the words. In this paper, we propose the use of information theory to quantify word context effect. In this way, the strength of word context effect will depend on the identity of the words. We apply the method to quantify word context effect in Chinese words. This knowledge is encoded in an artificial neural network using the interactive activation and competition model. The network is used to recognize Chinese characters and we are able to achieve a high recognition rate.