Abstract. License plate recognition (LPR) is a fundamental component
of various intelligent transport systems, which is always expected to be
accurate and efficient enough. In this paper, we propose a novel LPR
framework consisting of semantic segmentation and character counting,
towards achieving human-level performance. Benefiting from innovative
structure, our method can recognize a whole license plate once rather
than conducting character detection or sliding window followed by percharacter recognition. Moreover, our method can achieve higher recognition accuracy due to more effectively exploiting global information and
avoiding sensitive character detection, and is time-saving due to eliminating one-by-one character recognition. Finally, we experimentally verify
the effectiveness of the proposed method on two public datasets (AOLP
and Media Lab) and our License Plate Dataset. The results demonstrate
our method significantly outperforms the previous state-of-the-art methods, and achieves the accuracies of more than 99% for almost all settings.