An Extensive Evaluation of Handwriting Analysis Methods for Personality Prediction

The practice of analyzing specific letters and writing styles, commonly referred to as graphology or handwriting analysis, is often considered a pseudoscientific endeavor. However, it suggests that a person's personality traits can be discerned from their handwriting, making it a valuable source of insight into an individual's character. Although it may not provide a comprehensive overview, handwriting analysis can unveil specific personality traits, tendencies, and characteristics. Handwriting serves as a window into one's genuine character, including emotional expression, fears, trustworthiness, and protective mechanisms. Notably, contemporary research integrates machine learning and deep learning techniques to advance the analysis of handwriting for personality prediction. These innovative approaches leverage large datasets and sophisticated algorithms to derive deeper insights into the relationships between handwriting patterns and personality traits, thereby enhancing the credibility and effectiveness of this method as a supplementary tool for understanding personality. This paper explores the integration of feature extraction, machine learning and deep learning techniques in the context of handwriting analysis, shedding light on their potential to revolutionize the field and provide a more comprehensive understanding of human personality. The research reveals that employing advanced machine learning improves handwriting analysis, uncovering nuanced connections between writing patterns and personality traits.