Style-Preserving Generator for Synthetic License Plate Recognition
Bibliographic record
Abstract
We propose the Style-Preserving Generator (SPG) to generate synthetic license plate data to train License Plate Recognition (LPR) models, and compare the performance with the same models trained on real-world data. The proposed SPG can edit the characters on real-world license plates while maintaining their original styles, allowing synthetic license plate data to be generated with user-specified characters. We can therefore synthesize license plates with desired characters to effectively alleviate the data attribute imbalance and privacy issues associated with real-world license plates. To the best of our knowledge, this work is the first study to present the making of synthetic LP data by proposing a novel text-editing approach tailor-made for LP data, that is the proposed SPG. The SPG consists of a transformer, a source encoder, a source style encoder, a character mask decoder, a target generator, and a target discriminator. Given a source license plate image and a specified text as input, these components collaborate to compute the self- and cross-attention embeddings, predict character masks, and generate a synthetic license plate in the source style but with source characters replaced by the specified characters. We adopt a two-phase training scheme. Phase 1 training uses synthetic data only, but Phase 2 training uses synthetic and real-life data. To showcase the effectiveness of the SPG, we introduce a new benchmark dataset, the LP-2025 (License Plate 2025), which alleviates the limitations of existing datasets and presents new challenges for license plate recognition and generative models. We validate SPG performance on the LP-2025 dataset and other benchmark datasets and compare it against state-of-the-art text-editing approaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".