Stroke features in the Chinese character recognition
Bibliographic record
Abstract
While line vertices, terminations, and midsegments are critical for Roman letter identification, the diagnostic features of Chinese character strokes remain unclear. This study examines how local stroke-level features and global line-relation mechanisms contribute to Chinese character recognition. In Experiment 1, we applied the Bubbles classification image technique to native Chinese readers to identify diagnostic stroke features. Results revealed four key features: horizontal hooks, dots, vertical turnings, and raises. These features, while analogous to terminations in alphabetic systems, reflect unique dynamics of Chinese stroke production, marking stroke origins and terminations. Experiment 2 employed a delayed-segment paradigm to assess functional significance of these features. Greater degradation of vertices and midsegments significantly prolonged reaction times, and removal of stroke-based terminations (e.g., hooks) impaired recognition accuracy. Together, these findings support a two-tiered hierarchy in Chinese character recognition: stroke-specific terminals enable fine-grained feature discrimination, while line-relation features support global structural integration. The results affirm script-general principles (midsegments and vertices as perceptual anchors) and highlight language-specific adaptations, where stroke terminations function as dynamic positional cues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".