Biomimetic pattern recognition for writer identification using geometrical moment functions

Abstract

Writer identification (WI) based on handwriting has a great significance in many real world applications, such as crime suspect, identification in forensic science, in the court of justice where one must come to a conclusion about the authenticity of a document, and authorship determination of historical manuscripts. WI emphasizes on identifying the authorship of handwriting while ignoring the connotation of the words in the documents. The samples of WI included in the training sample sets have no prior knowledge between the same classes of samples. While biomimetic pattern recognition (BPR) has unique characteristics of accepting the samples which means the difference between two samples of the same class must be gradually changed. Unlike classical WI procedure, BPR uses the concept of cognition in which when new feature of handwriting samples are fed to the classifier, only these samples will be trained accordingly. Therefore, this study focused on the concept of BPR based on the principle of homology-continuity (PHC), hyper sausage neuron network (HSNN), and three weight neuron network (TWNN). PHC is the prior knowledge to be applied into the distribution of sample data in BPR. While, HSNN’s coverage in high dimensional space with feature space for covering the distribution area of the sampling points in the same class constructed a sausage shape, TWNN made triangle shape. The identification process of the samples in HSNN and TWNN coverage depends on the proposed threshold values. This study found that the results of the proposed methods were better in identifying the authorship of handwriting with an accuracy of more than 95% using various features of geometrical moment functions.