Stroke Structure Reconstruction and Virtual Display System Design for Calligraphic Seal Engraving Using Artificial Intelligence
Bibliographic record
Abstract
Aiming at the needs of reconstructing the structure of calligraphic seal cutting strokes and virtual display, this study designs a GAN technique that integrates three models, namely, "WGAN, DCGAN and CGAN".The CycleGAN model is used to obtain the mapping relationship between learning and style migration by utilizing its cyclic consistency loss.Adaptive pre-morphing technique is introduced to process the input image to capture the outline information and morphological features of calligraphic seal carvings, and a Generative Adversarial Network-based Generative Model for Structural Reconstruction of Calligraphic Fonts (CRA-GAN) is proposed.Meanwhile, an online virtual display system is designed to provide users with a good sense of experience in the virtual display of calligraphy.The results show that the CRA-GAN model can better capture the details and global information of the fonts, and its recognition rate of the eight calligraphic fonts ranges from 90.42% to 97.38%, and the MOS rating value of the text image is > 8.5 points, and its recognition results are in line with the observation characteristics of the human eye for calligraphic images.The FID calculation result of the CRA-GAN method ( 204.361) of the CRA-GAN method is much lower than that of other methods, which obviously improves the diversity and visual quality of the generated calligraphic fonts.This paper evaluates the user's experience of the system from five aspects: narrative experience, emotional experience, sensory experience, cognitive experience and interactive experience, and calculates that the final score of the system is in the range of 80-100, which indicates that the user's satisfaction is very high after actually experiencing the virtual display system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".