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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".