Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. existing ALPR methods are focused on specific application scenarios. Although there are a few methods focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems high efficiency. We design alight LPD structure based on efficient object detection methods and use anchor free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficientalpr-unconstrained.