Rethinking and Designing a High-performing Automatic License Plate Recognition Approach

Abstract

In this paper, we propose a real-time and accurate automatic license plate recognition (ALPR) approach. Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets. Based on these insights, we propose a novel ALPR approach, termed VSNet. Specifically, VSNet includes two CNNs, i.e., VertexNet for license plate detection and SCR-Net for license plate recognition, integrated in a resampling-based cascaded manner. In VertexNet, we propose an efficient integration block to extract the spatial features of license plates. With vertex supervisory information, we propose a vertex-estimation branch in VertexNet such that license plates can be rectified as the input images of SCR-Net. In SCR-Net, we introduce a horizontal encoding technique for left-to-right feature extraction and propose a weight-sharing classifier for character recognition. Experimental results show that the proposed VSNet outperforms state-of-the-art methods by more than 50% relative improvement on error rate, achieving > 99% recognition accuracy on CCPD and AOLP datasets with 149 FPS inference speed. Moreover, our method illustrates an outstanding generalization capability when evaluated on the unseen PKUData and CLPD datasets.

13 pages. Accepted for Publication at IEEE Transactions on Intelligent Transportation Systems

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• Squeeze-and-Excitation Networks
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Cited by in corpus (10)

• On the Cross-dataset Generalization in License Plate Recognition
• Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
• A First Look at Dataset Bias in License Plate Recognition
• Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
• Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
• Leveraging Model Fusion for Improved License Plate Recognition
• Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark
• Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach
• Character Time-series Matching For Robust License Plate Recognition
• Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation