Paper Title
HANDWRITING ANALYSIS USING DEEP LEARNING MODELS TO PREDICT PERSONALITY TRAITS

Abstract
Handwriting is a complex neuro-motor activity influenced by cognitive, emotional, and psychological processes. Because of this close connection between the brain and motor expression, handwriting has long been studied as a potential indicator of personality traits and psychological states. This research paper explores personality analysis using handwriting by synthesizing existing literature from graphology, psychology, forensic science, and computational handwriting analysis. The study reviews traditional graphological concepts, modern computational techniques, and empirical findings related to personality traits and mental conditions reflected in handwriting. The paper also proposes a general methodology for handwriting-based personality analysis using digital image processing and machine learning techniques. The findings suggest that while handwriting can provide supportive behavioral indicators, it should be used as a complementary tool rather than a standalone diagnostic method. Keywords - Convolutional Neural Network, Deep Learning, Handwriting Analysis, Personality Analysis.