Improved Methodology For Personality Assessment Using Handwritten Documents

Abstract

This paper presents an analysis and design of an intelligent software that supports handwriting analysis-based system development. The software facilitates handwriting acquisition and collection, graphological analysis, and results sharing. A substantially large number of handwriting samples, collected and analyzed over a long period of time, should help in discovering the ground truths needed to validate and justify applications in biometrics, medicine, forensic, psychiatry, patient monitoring and the like domains. Although, studies indicate strong relationships between consciousness that results into graphic patterns like handwriting, but the ground truth would give further insight into the phenomenon that governs graphology processes. The paper also presents rules through feature learning and rule induction modules, and embedding intelligence through an inference mechanism so a system build using these features justify its decision. In these experiments 1000 number of people participated from CPAR dataset. The average acceptance percentage range between 84 to 96 percentages.