A Comparative Study of Feature Selection

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification. Various filter and wrapper feature selection methods are selected and their performances are analyzed. This paper describes an improved sequential forward feature selection method besides the exploration of significant features for invarianceness of authorship from global shape features by using various feature selection methods. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship.