A License Plate Tilt Correction Algorithm Based on the Character Median Line Algorithme de correction d’inclinaison de plaque d’immatriculation basé sur la ligne médiane du caractère
Bibliographic record
Abstract
License plates intelligent identification systems must be able to correct the tilt of a license plate in an image. Aiming at improving on the low tilt accuracy, complex algorithms, and weak robustness against noise of existing tilt correction methods, we proposed an algorithm based on the character median line. The license plate image is first preprocessed, and a projection method is applied to find and segment the character region, resulting in a license plate with no border. For the no border license plate image, we then fix x-coordinates, and find the maximum and minimum values of y-coordinates, and put them into a matrix. The next step is to obtain the mean value of the maximum and minimum values of y, obtain the point sets on the character median line of the license plate, and remove the singular points using a threshold. Finally, a straight line is fitted using the least-squares method, and the tilt angle is obtained by applying a formula for the slope and the angle. For a tilted and damaged license plate, experiments show that the proposed algorithm is simple, has a low error ratio, and has good robustness against noise and deformation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".