Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not
received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of
feature selection in this domain. Various filter and wrapper feature selection methods are selected and their
performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of
features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of
these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the
future works to fully compatible with Writer Identification domain