Revealing Personality through Handwriting: A Fusion of Graphology and Machine Learning Techniques
Bibliographic record
Abstract
Abstract This paper explores the integration of graphology and machine learning to analyze personality traits through handwriting. The research is motivated by the understanding that the brain expresses personality traits through neuromuscular movements, particularly in handwriting. By bridging historical graphological methods from the 19th century with contemporary machine learning techniques, this study utilizes a diverse dataset of 1108 handwriting image samples, sourced from Centre for Pattern Recognition and Machine Intelligence (CENPARMI) and a graphology expert. We employed machine learning algorithms such as k-nearest neighbor (k-NN), random forest, logistic regression, and transfer learning method, along with synthetic minority oversampling technique (SMOTE) for data balancing and ensemble methods like majority voting and stacking to classify and mine the images. Our experimental results indicate a significant improvement in prediction accuracy, exceeding 90% for traits like “Agreeableness” and “Open to Experience” using the stacking method. This research makes three key contributions: the innovative integration of graphology with machine learning for personality assessment, methodological advancements in handling imbalanced datasets, and the application of transfer learning in handwriting analysis. The findings illustrate the potential of this interdisciplinary approach to enhance personality trait prediction accuracy, offering valuable insights for psychology and personalized services. This study opens new avenues for future research in personality psychology and related fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".