Abstract
Automatic license plate recognition has a wide range of applications in intelligent transportation systems and is of great significance. However, most of the current work on license plate recognition focuses on the images on the front of license plates. license plate recognition in natural scenes and arbitrary perspective is still a huge challenge. To solve this problem, this work mainly studies the detection and recognition of inclined Chinese license plates in natural scenes. We propose a robust method that can detect and correct multiple license plates with severe distortion or skewing in one image and input them into the license plate recognition module to obtain the final result. Different from the existing methods of license plate detection and recognition, our method performs affine transformation during license plate detection to rectify the distorted license plate image. It can not only avoid the accumulation of intermediate errors but also improve the accuracy of recognition. As an additional contribution, we put forward a challenging Chinese license plate recognition data set, including images obtained from different scenes under a variety of weather conditions. Through a large number of comparative experiments, we have proved the effectiveness of our proposed method.
Highlights
With the latest developments in intelligent transportation and deep learning (DL), Automatic License Plate Recognition (ALPR) has become an important frontier field of research [1]
Most of the current traffic applications, including traffic flow monitoring and parking lot access verification, involve license plate recognition (LPR) that is performed by an ALPR system [2]
ALPR consists of 3 steps: vehicle detection, license plate detection (LPD), and LPR [4], [5], [30])
Summary
With the latest developments in intelligent transportation and deep learning (DL), Automatic License Plate Recognition (ALPR) has become an important frontier field of research [1]. LICENSE PLATE DETECTION The previous researchers usually used manual features and classic machine learning classifiers, [24] with image binarization and grayscale analysis to fix the license plate area Their mainstream methods can be divided into three categories: edge-based, color-based, and texture-based. For each ALPR stage, CNNs are trained and fine-tuned (for example, changes in cameras, lights, and background) to ensure they are robust under complicated conditions Whereas, it is not easy for the YOLO network to detect small-sized objects, so we need to further evaluate the scene where vehicles are away from the camera. Recurrent Neural Networks (RNNs) have been extensively studied It encodes words or text lines into a feature sequence, which can be recognized without character segmentation.
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