A Single Neural Network for Mixed Style License Plate Detection and Recognition
Qiuying Huang, Zhanchuan Cai, Ting Lan
Abstract
Most existing methods for automatic license plate recognition (ALPR) focus on a specific license plate (LP) type, but little work focuses on multiple or mixed LPs. This article proposes a single neural network called ALPRNet for detection and recognition of mixed style LPs. In ALPRNet, two fully convolutional one-stage object detectors are used to detect and classify LPs and characters simultaneously, which are followed by an assembly module to output the LP strings. ALPRNet treats LP and character equally, object detectors directly output bounding boxes of LPs and characters with corresponding labels, so they avoid the recurrent neural network (RNN) branches of optical character recognition (OCR) of the existing recognition approaches. We evaluate ALPRNet on a mixed LP style dataset and two datasets with single LP style, the experimental results show that the proposed network achieves state-of-the-art results with a simple one-stage network.