no code implementations • 30 Nov 2020 • Yi Wang, Zhen-Peng Bian, Yunhao Zhou, Lap-Pui Chau
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.
no code implementations • 19 Apr 2020 • Zhiyu Zhu, Zhen-Peng Bian, Junhui Hou, Yi Wang, Lap-Pui Chau
However, the existing networks usually suffer from either redundancy of convolutional layers or insufficient utilization of parameters.
no code implementations • 26 Sep 2019 • Yi Wang, Zhen-Peng Bian, Junhui Hou, Lap-Pui Chau
That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures.