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Automated license plate recognition (LPR) systems have garnered substantial attention within the field of intelligent transportation systems, owing to their pivotal role in facilitating toll collection, enhancing traffic management, and ensuring operational efficiency. Despite recent breakthroughs in convolutional and recurrent neural network architectures, Polish LPR remains underexplored, with most existing approaches relying on conventional optical character recognition. This study proposes a hybrid convolutional neural network – recurrent neural network (CNN-RNN) model equipped with a Thin-Plate Spline (TPS) transformation module, a ResNet-based feature extractor, a bidirectional Long Short-Term Memory (LSTM) sequence model, and an attention-based decoder to address the unique challenges of Polish license plates. The model is trained on a high-difficulty dataset, comprising real-world images without explicit character-level bounding boxes. Empirical evaluations underscore the efficacy of the proposed system, with competitive accuracy and normalized edit distance scores achieved on Polish, Czech, Hungarian, and Slovak datasets. Additionally, transfer learning from closely related Central European plate formats to Polish data demonstrates marked improvements in convergence and overall performance. Further validation on a challenging video-based dataset reveals the robustness of the proposed approach, evidencing its potential applicability in real-world scenarios and highlighting majority voting as an effective strategy to enhance system reliability under variable conditions.
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