A new feature ranking method in a HMM-based handwriting recognition system

In this paper, we propose a new feature ranking method in a recognition system, by introducing the concept of the effectiveness of the distinguishing power of features and considering the correlation among features. To find the subset of most important features, first, the best feature can be identified by its effective distinguishing power and put in an empty feature set. Then, each of the remaining features is ranked based on their effective distinguishing capacity contribution and the highest-ranked feature is added to the selected subset. This process is repeated till the performance of the system reaches its peak or the effective distinguishing contribution falls below a certain value. The application of this method to an existing handwriting recognition system showed strong support for our methodology of feature ranking.