A Structured Framework for Formalized and Quantitative Handwriting Examination

The demand for a formalized and transparent approach to handwriting assessment has long been recognized within forensic and legal contexts. A structured methodology not only reduces interpretative subjectivity but also enables quantifiable measurement and ensures greater consistency in evaluations. This article presents a practical framework that models the degree of similarity between handwriting samples—texts and signatures—through a two-stage process: feature-based evaluation and congruence analysis. Both stages produce quantitative markers that are integrated into a unified similarity score, forming the foundation for more complex comparisons involving multiple questions and known texts. The proposed procedure, which is the major result of the paper, is not merely theoretical; it has been applied in real forensic casework, yielding preliminary statistical outcomes. In particular, it demonstrates the discriminative power of different handwriting features. The paper also discusses future directions for development, with a focus on the integration of artificial intelligence (AI) to enhance specific components of the assessment process.