Graphology-based character assassination is one of the popular ways to identify the personality of any human being. This study presents a novel approach to personality assessment through handwriting analysis, leveraging attention-based deep neural networks to extract and interpret graphological features. We deploy a convolutional neural network (CNN) in our model to extract the low-level graphological features, which are then followed by an attention mechanism for personality inference by the most salient elements of handwriting. Use these features to process a transformer-based network that would grasp underlying handwriting characteristics that are signaling personality traits. We hold the data on a writing template that is wide-ranging and comes with validated personality assessments, which we test with the help of our model. We can pick up improvements over the use of traditional graphology methods and machine learning novice steps in our experiments. According to our outcomes, we can make an accuracy ratio of over 85% on the Big Five personality traits quiz, turning conscientiousness and neuroticism into strong suits. This study presents a milestone in automating the process of personality assessment but does not as well bring along the implications concerning handwriting patterns we need to gain some new insight.