Machine Learning Puts Handwriting-Based Personality Analysis to a Rigorous Test

For decades, graphologists have claimed that the slant of a letter, the pressure of a pen stroke, or the spacing between words can reveal the hidden contours of a person’s character. The idea has proved remarkably durable, resurfacing in the modern era dressed in the language of artificial intelligence. Now, a study published in Discover Artificial Intelligence has subjected those claims to one of the most methodologically demanding examinations to date, and the verdict is stark: manually coded handwriting features did not predict questionnaire-derived Big Five personality scores any better than a model that simply guessed the average for everyone.

The research, conducted by Sheetal Thomas of Parul University, Manjeevan Seera of Monash University Malaysia, and Mridula Goel of BITS Pilani, K. K. Birla Goa Campus, drew on data from more than three hundred participants recruited through universities, factories, government organisations and corporate offices across four districts of Gujarat, India. Each participant completed a sixty-item personality questionnaire covering the five major domains of the Big Five framework — Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness — and copied a standard passage known as the London Letter, a text deliberately chosen because it contains a broad range of letters, words and punctuation that makes visual characteristics comparable across writers.

The handwriting specimens were then translated into numbers. A coder with roughly twelve years of specialist handwriting-analysis training, working under the Hilliger Assessment Guidelines supplied by the British Institute of Graphologists, recorded between sixteen and twenty-six binary features for each personality domain. Slant, size, spacing, pressure, margins, baseline direction, letter forms and connecting strokes were each scored as present or absent. The personality outcomes, meanwhile, were computed as continuous scores: the mean of twelve five-point Likert items per domain, yielding values on a scale from one to five rather than the binary personality labels that have complicated much of the earlier literature.

What makes this study unusual is not the data itself but the rigor of its evaluation. The researchers first audited the source workbooks, excluding sixty-two pilot-phase records and screening out incomplete, invalid or invariant questionnaires within each trait separately. That produced analytic samples ranging from 320 to 330 participants per domain. They then compared two quite different machine-learning models — elastic-net regression, a regularised linear method that shrinks weak coefficients, and histogram-based gradient boosting, a non-linear ensemble of constrained regression trees — against a deliberately humbling benchmark: a mean-prediction baseline that ignores the handwriting entirely. Every model was evaluated with five-fold cross-validation repeated five times, generating twenty-five out-of-sample test folds for each model and domain combination.

The results were unambiguous. Neither model consistently improved on the mean baseline in mean absolute error, the study’s primary metric. For Neuroticism, the baseline error was 0.3102 score units while elastic net managed 0.3104 — a difference in the fourth decimal place. Extraversion and Openness told the same story. Agreeableness was the single domain where elastic net edged out the baseline, but the advantage amounted to roughly 0.0011 score points, about one-thousandth of a single point on the five-point scale. Gradient boosting actually performed worse than the baseline in every domain. Most tellingly, mean cross-validated R² was negative for every model-domain combination, meaning that in held-out data the models predicted worse than simply predicting the training-set average.

Permutation tests reinforced the conclusion. When the researchers randomly shuffled the outcome values fifty times and re-evaluated each model, none of the observed model performances stood out from what chance would produce. The smallest permutation p-value was 0.078, for elastic net on Agreeableness, and the rest ranged far higher. The authors are careful to note that these exploratory tests used a limited number of shuffles, but taken together with the negative R² values and the negligible error reductions, the pattern points firmly in one direction: the handwriting-feature matrices contained no reliable information about the questionnaire-derived scores in this sample.

Perhaps the most interesting twist lies in what did work. Two independent, practising graphologists, blinded to all questionnaire data and to each other’s ratings, separately coded twenty-six common handwriting samples. They agreed with one another to a striking degree: mean feature-level percentage agreement ranged from 94.1 percent to 97.8 percent across domains, and mean Cohen’s kappa — a statistic that corrects for chance agreement — ranged from 0.803 for Neuroticism to 0.929 for Conscientiousness. In other words, the features could be identified reproducibly. Yet this reliability did nothing to establish validity. As the authors emphasise, inter-rater reliability and criterion validity answer different questions: one asks whether coders see the same thing, the other whether that thing predicts an independent measure of the construct. A feature can be recognised with near-perfect consistency and still carry no psychological meaning.

The study also probes the quality of the reference measures themselves. Internal consistency of the study-specific questionnaire varied substantially, with Cronbach’s alpha of 0.466 for Neuroticism and 0.675 for Openness — below the commonly cited 0.70 threshold — against 0.805 for Conscientiousness. The authors acknowledge that measurement error of this kind can attenuate any true association and cap the achievable predictive performance, so the low reliabilities limit how strongly the negative result can be generalised. But they are equally clear that weak questionnaire reliability does not rescue handwriting-based claims; it simply constrains conclusions in both directions. The study establishes that these specific coded features did not predict these specific scores, not that no conceivable handwriting representation could ever relate to a better-validated personality instrument.

The context of earlier work matters here too. Parts of the same Gujarat dataset had been used in four previous publications, covering image-based detection of Extraversion- and Neuroticism-associated features, sentiment and risk profiling from handwritten text, and questionnaire-based links between personality and mutual-fund investment preferences. The authors stress that this new paper is not a replication or repudiation of those studies but a different analytical task with a stricter estimand: predicting an independently constructed continuous personality score from the complete feature matrix, out of sample. Earlier feature-detection results, they argue, should not be read as demonstrations that handwriting can recover continuous personality scores in unseen individuals without further evidence.

The broader implications reach well beyond graphology. Personality has been linked in the behavioural-finance literature to risk tolerance, investment preferences and planning behaviour, and that association creates commercial temptation to score personality from cheap, easily collected behavioural traces — including handwriting — in finance, employment, insurance and other high-stakes settings. This study demonstrates why such shortcuts are dangerous. A technically reproducible coding pipeline feeding a properly implemented machine-learning model can still fail completely at the criterion it claims to measure, and algorithmic output lends an unwarranted air of objectivity to constructs that lack validated measurement. The authors recommend independent, preregistered, externally validated evidence, subgroup fairness analyses and clear incremental predictive value before any such tool touches consequential decisions. In publishing a transparent negative result with complete data and code — the coded features are archived on Harvard Dataverse and the full Python workflow is released — the team offers a template for how behavioural AI claims should be tested: clear outcome construction, a simple baseline, repeated out-of-sample evaluation and the honesty to report when the signal simply is not there.

Subject of Research: Predictive validity of manually coded handwriting features for questionnaire-derived Big Five personality scores

Article Title: Predictive validity of manually coded handwriting features for questionnaire derived personality scores

Article References: Predictive validity of manually coded handwriting features for questionnaire derived personality scores. (n.d.). https://doi.org/10.1007/s44163-026-02132-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02132-z

Keywords: graphology, handwriting analysis, Big Five personality, predictive validity, machine learning, elastic net, gradient boosting, cross-validation, inter-rater reliability, negative result, psychometrics, behavioural AI