This paper presents a character recognition method based on a dynamic model, which can be applied to character patterns from both on-line and off-line input. Other similar attempts simply treat on-line patterns as off-line input, while this method makes use of the on-line input's characteristics by representing the time information of handwriting in the character pattern representations. Experiments were carried out on the Hiragana character set. Without non-linear normalization, this method achieved recognition rates of 92.3% for on-line input and 89.1% for off-line input. When non-linear normalization is used, there is an increase in performance for both types of input with on-line input achieving 94.5% and off-line input achieving 94.1%. The reason for the difference in the effectiveness of non-linear normalization on off-line and on-line patterns could be that while the method used for off-line input was an established and proved one, we used our own initial attempt at non-linear normalization for the on-line patterns. If the same level of effectiveness of non-linear normalization as off-line input is achieved on the on-line input, however, the recognition rate for on-line input again improves becoming 96.3%. Since only one standard pattern was used per category for the dictionary patterns, the above results show the promise of this method. This result shows the compatibility of this method to both on-line and off-line input, as well as its effective use of on-line input's characteristics. The effectiveness of this use of the time information is shown by using an actual example. The data also shows the need for a method of non-linear normalization which is more suitable for on-line input.