Abstract
Automatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. This work presents an end-to-end ALPR method based on a hierarchical Convolutional Neural Network (CNN). The core idea of the proposed method is to identify the vehicle and the license plate region using two passes on the same CNN, and then to recognize the characters using a second CNN. The recognition CNN massively explores the use of synthetic and augmented data to cope with limited training datasets, and our results show that the augmentation process significantly increases the recognition rate. In addition, we present a novel temporal coherence technique to better stabilize the OCR output in videos. Our method was tested with publicly available datasets containing Brazilian and European license plates, achieving accuracy rates better than competitive academic methods and a commercial system.
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