Аннотация
The article is devoted to the development and application of an algorithm for analyzing graphomotor writing trajectories in digital educational systems using the Catmull-Rom spline. The problem of improving the accuracy and stability of processing user input received from touch devices (stylus or finger) in the context of digital writing learning is considered. The proposed method includes the stages of coordinate data collection, normalization, noise filtering, and trajectory smoothing. The Catmull-Roma spline is used to ensure the continuity of curves and preserve key motion features, which increases the accuracy of comparison with reference patterns. The paper presents a computational algorithm that implements an automatic assessment of the quality of letter writing based on a number of parameters: curvature, angular deviations, speed stability and the number of strokes. The algorithm is integrated into a digital diagnostic system capable of generating detailed reports, identifying common errors, and offering personalized tasks. The developed solution can be used in intelligent learning platforms, biometric authentication systems, neuropsychological diagnostics and computer forensics. The proposed approach demonstrates high adaptability to various input scenarios and provides the basis for building more complex machine learning systems focused on recognizing, analyzing, and generating handwritten text.