Hierarchical flexible matching for recognition of Chinese characters

Although there are thousands of many commonly used Chinese characters, they are actually composed of a lower number of stroke sub-patterns. Thus, in the task of recognizing optical Chinese characters, it is worthwhile to first identify the sub-patterns and then the whole characters composed of them. In such a hierarchical approach, however, decision mistakes at lower-levels can easily propagate into upper-levels to cause a high mis-recognition rate. To remedy this problem we devise a method called hierarchical flexible matching (HFM). The idea is to minimize the decision burdens at lower levels by allowing possibly conflicting sub-patterns to be identified from the same pool of primitives. The collection of these sub-patterns is then matched against some pre-specified models. In doing so, a metric is used to measure how well a candidate model is mapped into the given collection and how many primitives are covered by this mapping. We apply the HFM method to the font-indepdendent recognition of printed Chinese characters and have acquired very promising results