Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios

TL;DR

This paper introduces a unified, end-to-end deep learning framework for real-time license plate detection and recognition in unconstrained scenarios, improving accuracy and speed over previous methods.

Contribution

It presents a lightweight, anchor-free detection method combined with a novel CNN for character feature extraction and sequence recognition, enabling simultaneous detection and recognition.

Findings

Outperforms state-of-the-art in speed and accuracy

Works effectively in diverse, unconstrained scenarios

Operates in real-time with a lightweight model

Abstract

Benefiting from the rapid development of convolutional neural networks, the performance of car license plate detection and recognition has been largely improved. Nonetheless, most existing methods solve detection and recognition problems separately, and focus on specific scenarios, which hinders the deployment for real-world applications. To overcome these challenges, we present an efficient and accurate framework to solve the license plate detection and recognition tasks simultaneously. It is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time. Specifically, for unconstrained scenarios, an anchor-free method is adopted to efficiently detect the bounding box and four corners of a license plate, which are used to extract and rectify the target region features. Then, a novel convolutional neural network branch is designed to further extract features of characters without segmentation. Finally, the recognition task is treated as sequence labeling problems, which are solved by Connectionist Temporal Classification (CTC) directly. Several public datasets including images collected from different scenarios under various conditions are chosen for evaluation. Experimental results indicate that the proposed method significantly outperforms the previous state-of-the-art methods in both speed and precision.