Text and User Generic Model for Writer Verification Using Combined Pen Pressure Information From Ink Intensity and Indented Writing on Paper

Abstract

Writer verification is a method to specify an authentic writer from handwriting. Automated writer verification methods are required for various applications (e.g., credit cards, checks, and passports). However, there is room for improvement in the performance of such methods compared with the performance of human beings, for example, forensic document examiners. Because automated writer verification systems do not always return correct results under any circumstances, which can lead to grave consequences, further research is required to improve the performance of such methods. Furthermore, problems caused by limited samples must be solved for real applications. To improve verification accuracy with limited samples, we propose a text and user generic model for writer verification that uses a combination of pen pressure information from ink intensity and writing indentations obtained by a multiband image scanner. We introduce a writer-specific dissimilarity representation to consider individual handwriting characteristics that affect model performance. Experimental results obtained using handwriting samples collected from 54 volunteers are reported. The results show a decrease in error rate compared with conventional methods from 10.0% to 4.0%.