Calligraphy practice method and system based on user calligraphy practice effect

By collecting writing trajectory data and using deep learning models to evaluate the quality of calligraphy practice, constructing multi-dimensional ability vectors and predicting future abilities, the problem of insufficient evaluation in existing systems is solved, personalized practice task recommendations are realized, and the efficiency and effectiveness of calligraphy practice are improved.

CN122048602APending Publication Date: 2026-05-15SHAANXI NORMAL UNIV
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Patent Information

Application Number
CN202610160711.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-04
Publication Date
2026-05-15

AI Technical Summary

Technical Problem

Existing calligraphy practice systems cannot comprehensively and accurately assess students' writing skills, especially in terms of complex factors such as the structure of Chinese characters, the strength of strokes, and the rhythm of writing. This leads to assessment bias and the recommended practice characters lack scientific rigor and personalization.

Method used

By collecting writing trajectory data, writing features such as character structure, pen pressure, and writing rhythm are extracted. A quality coefficient is generated using a deep learning model, a multi-dimensional ability vector is constructed, a time-series prediction model is trained to predict future abilities, and personalized practice tasks are generated using a symbolic regression method.

Benefits of technology

It enables precise, multi-dimensional assessment of handwriting quality, dynamically adapts to the user's ability status, provides personalized practice tasks, and significantly improves practice efficiency and effectiveness.

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Abstract

The invention discloses a calligraphy practice method and system based on a calligraphy practice effect of a user. The method comprises the following steps: collecting writing track data and writing statistical data in a calligraphy practice process of the user; based on the writing track data, extracting writing characteristics of the user in three dimensions of a font structure, pen wielding strength and writing rhythm, and fusing a quantitative index and a deep learning score to obtain a quality coefficient of each dimension; constructing a multi-dimensional capability vector and a capability time sequence of the user based on the quality coefficient; training a time sequence prediction model based on the ability time sequence and the writing statistical data; constructing a sample based on the writing statistical data, generating a prediction label by using the prediction model, and generating a symbolic rule for describing a relationship between the practice behavior and the ability change by using a symbolic regression method in a fitting manner; and for the candidate Chinese character set, predicting an expected capability improvement amount after practicing each Chinese character in combination with a symbol rule, calculating a comprehensive benefit value of each Chinese character by integrating the expected improvement amount and the current short plate weight of each dimension of the user, and selecting a target practicing character set to generate a personalized practicing task according to the comprehensive benefit value. According to the invention, accurate evaluation and personalized intelligent recommendation of calligraphy practice are realized.
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