Personality trait identification through handwriting analysis presents a challenging area within automated document recognition based on Artificial Intelligence solutions. Recent studies relied on solutions automating graphonomic processes, while others address only a few local features, conversely few studies offer solutions based on textural features. In this work, we propose an automated approach for personality trait identification that treats a scripter’s handwriting as a texture by leveraging a diverse set of textural features, including LCP, oBIFCs, LPQ, LBP, among others. The approach is validated on FFM-annotated datasets using cost-effective classifiers such as XGBoost, Random Forest, Gradient Boost, SVM, and Naive Bayes. Our empirical study enabled the judicious selection of the most suitable textural features for each personality trait. Subsequently, we constructed a comprehensive personality trait identification solution by combining multiple textural features and integrating top-performing classifiers. The experimental results demonstrated the validity of our hypothesis, achieving performance improvements of more than 10% on both datasets.