Improvement of Authorship Invarianceness for Individuality Representation in Writer Identification

Writer identification (WI) is one of the areas in pattern recognition that have created a center of attention by many researchers to work in. Recently, its focal point is in forensics and biometric application as such the writing style can be used as biometric features for authenticating individuality uniqueness. Existing works in WI concentrate on feature extraction and classification task in order to identify the handwritten authorship. However, additional steps need to be performed in order to have a better representation of input prior the classification task. Extracted features from the feature extraction task for a writer are in various representations which degrade the classification 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