Abstract
One of the most popular areas of study in pattern recognition which has now become the centre of many researchers' attention is Writer Identification. A more recent development in the area is Twins Handwriting Identification which has now become not only an important, but also widely popular area of study especially in the fields of forensic research and biometrical identification. In terms of biometrical identification, it is known that a pair of twins may share various similar traits genetically. Forensic evidence can be easily obtained from handwriting samples. Therefore, in order to achieve reliable and accurate identification based on handwriting, it is important for the similarities in the writing traits of a pair of twins to be differentiated. In identifying an individual, handwriting style can be analyzed to allow the implicit representation of the unique hidden features of the individual's handwriting. Said unique features can help in identifying the writer of the text which can be essential when identifying the writer between a pair of twins. Previous studies in authorship identification were highly concentrated in the study of the classification task as well as features extraction. However, the issue of the similarities in the traits of a pair of twins' handwriting were not taken into account thus, leaving a high possibility of degrading the performance of the classification process. Therefore, in order to achieve better input for the classification task, this article will discuss an additional process which can better represent an individual's personal features through the transformation of the similarities via discretization protocol. The additional process can help improve the level of identification for Individuality of Handwriting of a pair of twins.
Highlights
1 Introduction Despite the advancement and technological achievement of the current age, documents are still printed on paper and widely exchanged, the need for Writer Identification (WI)
The calculation for the authorship invarianceness for post- and pre-discretized feature vectors can be achieved through analyzing the intra-class and the inter-class of the Mean Absolute Error (MAE) value
The result of the analysis show that the use of post-discretized feature vector feature provides improved authorship invarianceness compared to the use of pre-discretized feature vector as the intra-class MAE value using the post-discretized feature vector is smaller and the inter-class MAE value is higher than that of the pre-discretized feature vector
Summary
Despite the advancement and technological achievement of the current age, documents are still printed on paper and widely exchanged, the need for Writer Identification (WI). The individual’s uniqueness can be computed according to each writer and the preservation of the information help ease the task of classification This discretization process proved to be beneficial in terms of nonlinear representation [23] and through the set of Figure 2 Different character between twins. The calculation for the authorship invarianceness for post- and pre-discretized feature vectors can be achieved through analyzing the intra-class and the inter-class of the MAE value. The hypothesis is proven correct and the discretization process is deemed able to improve the authorship invarianceness with the standard representation of the individual’s unique features presented clearly to help identify the writer between a pair of twins. This proves that better identification and higher level of accuracy can be achieved with the use of post-discretized datasets
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