LPR-Net: Recognizing Chinese license plate in complex environments

Abstract

License plate recognition (LPR) technology has been attracting increasing interest during recent years for its exclusive role in real world intelligent traffic management systems. Owing to its importance, numerous LPR methods have been developed. These methods are generally composed of three processing steps, i.e. license plate location, character segmentation and character recognition. However, the three-step scheme always yields unsatisfactory recognition performance in challenging complex environment like uneven illumination, adverse atmospheric conditions, complex backgrounds, unclear vehicle plates, low-quality surveillance camera, etc. In such scenes, the obtained license plates are usually not clear, which will cause imprecise results of localization and segmentation. Consequently, the recognition capacity is inadequate as its performance highly depends on the effects of localization and segmentation. To address these challenges, we propose a novel Chinese vehicle license plate recognition method to directly recognize license plate through an end-to-end deep learning architecture named license plate recognition net (LPR-Net). The LPR-Net is a hybrid deep architecture that consists of a residual error network for extracting basic features, a multi-scale net for extracting multi-scale features, a regression net for locating plate and characters, and a classification net for recognition. Moreover, an effective scheme based on batch normalization is used to accelerate training speed in the learning procedure. Extensive experiments demonstrate that the proposed method achieves excellent recognition accuracy and works more robustly and efficiently compared with the state-of-the-art methods in complex environments.