Handwriting analysis offers a valuable tool for predicting personality traits, as individuals typically find it challenging to mask their personality in their handwriting. Despite awareness of handwriting analysis techniques, consistently altering one’s writing to obscure personality traits is difficult. Continuous writing naturally reveals aspects of the writer’s personality through pen movements and strokes. Furthermore, compared to other biometric features such as fingerprints, irises, or facial recognition, handwriting is easier to obtain and involves fewer privacy concerns, aside from personal signatures and private diaries. Our study leverages the integration of psychological questionnaires and the expertise of graphologists to enhance handwriting analysis. By employing deep learning methods from the field of computer vision, we aim to extract more complex features from handwriting samples, thereby improving prediction accuracy compared to traditional machine learning approaches. This innovative approach capitalizes on the strengths of deep learning to advance the field of personality prediction through handwriting analysis.