Annotation-Free Character Recognition for Azerbaijani License Plates: GAN Generator Diversity and Multi-Architecture Detection

Abstract

This paper presents an automatic license plate recognition framework for Azerbaijani plates featuring annotation-free character recognition, addressing limited supervision, severe class imbalance, and unseen character combinations in low-resource settings. Unlike conventional approaches that require extensive character-level annotations, the proposed method uses synthetically generated images from generative adversarial networks to support few-shot and zero-shot learning without manual labeling. The study evaluates detection, generation, and recognition across three stages. Detection compares architectures under public pre-training, lightweight fine-tuning, and training from scratch on approximately 370,000 public and 412,000 private images. Generation compares four network types under controlled scaling; recognition measures synthetic-to-real transfer across fourteen dataset configurations. Five epochs of fine-tuning match or exceed the performance of training from scratch; YOLO11-S reaches a mean average precision of 0.9637 averaged over intersection-over-union thresholds from 0.5 to 0.95. Image quality metrics poorly predict recognition utility: the Wasserstein generative adversarial network with gradient penalty scores best on perceptual metrics yet yields the weakest recognition. Generator diversity dominates, with multi-generator ensembles reaching average precision of 0.714 at intersection-over-union 0.5 without real character annotations. These findings support an efficient annotation-free pipeline in low-resource settings. Source code for detection, synthetic generation, and recognition is publicly available; the repositories do not redistribute the datasets used in this study.