Big Five Personality Traits

Handwriting Analysis has been used for a very long time to analyze an individual’s suitability for a job, and is in recent times, gaining popularity as a valid means of a person’s evaluation. Extensive Research has been done in the field of determining the Personality Traits of a person through handwriting. We intend to analyze an individual’s personality by breaking it down into the Big Five Personality Traits using their handwriting samples. We present a dataset that links personality traits to the handwriting features. We then propose our algorithm - consisting of one ANN based model and PersonaNet, a CNN based model. The paper evaluates our algorithm’s performance with baseline machine learning models on our dataset. Testing our novel architecture on this dataset, we compare our algorithm based on various metrics, and show that our novel algorithm performs better than the baseline Machine Learning models.

psychological research, it often suffers from label interference, vocabulary-driven overfitting, and limited labeled datasets. As a result, models are brittle: they can fail with small training samples and behave inconsistently across trait ranges. To address this, we employ a practical single-trait approach that uses five independent ELECTRA-based classifiers, each corresponding to one of the big five dimensions, and trained them as separate binary tasks to prevent cross-trait interference. To reduce lexical bias and double the Pennebaker and King essay corpus from 2,467 to 4,934 samples, the team applied careful synonym-replacement augmentation using WordNet and additionally incorporated contextual augmentation generated by the Gemma model. Models were adjusted methodically to ensure fair comparisons. With test AUCs above 0.75, the ensemble achieves an average test accuracy of 0.724 on the Pennebaker and King benchmark, with per-trait accuracies of 0.72, 0.71, 0.74, 0.73, and 0.72 for openness, conscientiousness, extraversion, agreeableness, and neuroticism (OCEAN), respectively. These results substantially reduce inter-trait interference while matching or surpassing LIWC baselines and other transformer approaches.