A Fast Feature Selection Model for Online Handwriting Symbol Recognition

Many feature selection models have been proposed for online handwriting recognition. However, most of them require expensive computational overhead, or inaccurately find an improper feature set which leads to unacceptable recognition rates. This paper presents a new efficient feature selection model for handwriting symbol recognition by using an improved sequential floating search method coupled with a hybrid classifier, which is obtained by combining hidden Markov models with multilayer forward network. The effectiveness of proposed method is verified by comprehensive experiments based on UNIPEN database