Intelligent Evaluation Method of Calligraphy Characters Based on Deep Stroke Extraction
Why this work is in the frame
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Bibliographic record
Abstract
Calligraphy is an important part of Chinese culture. And calligraphy education is the main way to spread calligraphy culture. Intelligent calligraphy evaluation can reduce dependence on experienced calligraphy teachers and effectively enhance the development of calligraphy culture. However, traditional intelligent calligraphy evaluation methods mostly focus on the whole and lack fine-grained analysis, which cannot form effective evaluation results. In this paper, we propose an intelligent evaluation method for calligraphy characters based on deep stroke extraction. By disassembling calligraphy character strokes, a more fine-grained evaluation of the writing results of a single stroke can be achieved. This method consists of two main parts: stroke extraction module that extracts single strokes through a structure deformable image registration-based stroke extraction model; evaluation module that provides detailed quantitative evaluation results from the whole character, radicals and single strokes. The experimental results show that our method can extract strokes of complex calligraphy characters and provide detailed evaluation results of calligraphy characters effectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it