Vehicle license plate detection using region-based convolutional neural networks

Abstract

Vehicle license plate (LP) detection is a relatively complex problem until we assume the use of a static camera, variations in illumination, known templates of the LP, guaranteed color patterns and other simple assumptions. Practical applications demand robust and generalized LP detection techniques to accommodate complex scenarios. This work suggests a new approach to solving this problem by treating the vehicle LP as an object. The primary focus of this study is to address following tasks associated with the challenge of LP detection: (1) LP detection in every frame of a video sequence, (2) detection of partial LPs and (3) detection of LPs with moving cameras and moving vehicles. The state-of-the-art object detection techniques, including convolutional neural networks with region proposal (RCNN), its successors (Fast-RCNN and Faster-RCNN) and the exemplar-SVM, are used in this work to provide solutions to the problem. The suggested study demonstrates better results in comprehensive tests and comparisons than other conventional approaches.