ML-Based Personality Detection Using Handwriting Analysis

Handwriting analysis, or graphology, serves as a significant psychological tool for deciphering human personality through idiosyncratic writing patterns. While these patterns reflect an individual’s cognitive and emotional makeup, traditional methods rely heavily on manual interpretation. This human-centric approach is often hampered by subjectivity, significant time constraints, and inherent inconsistencies between different analysts, limiting its reliability in professional settings. To address these challenges, this study introduces an automated framework for personality detection leveraging machine learning. The proposed system integrates advanced image processing with a Convolutional Neural Network (CNN) to autonomously extract and analyse critical stylistic features, including slant, word spacing, stroke dynamics, and baseline alignment. By digitizing the feature extraction process, the model classifies personality traits with a level of precision and consistency that surpasses conventional manual techniques. Deep learning effectively modernizes behavioural analysis, providing a scalable, objective tool for recruitment and psychological profiling. By merging traditional graphology with computational intelligence, this approach offers a robust framework for understanding human behaviour.