Improvement of authorship invarianceness for individuality representation in writer identification

Abstract

Writer Identification (WI) is one of the areas in pattern recognition that have created a center of attention for many researchers to work in. Recently, its main focus is in forensics and biometric application, e.g. writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on feature extraction and classi?cation task in order to identify the handwritten authorship. However, additional steps need to be per- formed in order to have a better representation of input prior to the classi?cation task. Features extracted from the feature extraction task for a writer are in vari- ous representations, which degrades the classi?cation performance. This paper will discuss this additional process that can transform the various representations into a better representation of individual features for Individuality of Handwriting, in order to improve the performance of identification in WI.