Revealing Personality through Handwriting: A Fusion of Graphology and Machine Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it