System of license plate recognition considering large camera shooting angles

The system of automatic license plate recognition (ALPR) is a combination of software and hardware technologies implementing ALPR algorithms. It seems to be easy to achieve the goal but recognition of license plate requires many difficult solutions to some non-trivial tasks. If the license plate is oriented horizontally, uniformly lighted, has a clean surface, clearly distinguishable characters, then it’ll be not too difficult to recognize such a license plate. However, the reality is much worse. The lighting of each part of the plate isn’t equal; the picture from the camera is noisy. Besides, the license plate can have a big angle relative to the camera and be dirty. These obstacles make it difficult to recognize the license plate characters and determine their location on the image. For instance, the accuracy of recognition is much worse on large camera angles. To solve these problems, the developers of automatic license plate recognition systems use a different approach to processing and analysis of images. The work shows an automatic license plate recognition system, which increases the recognition accuracy at large camera angles. The system is based on the technology of recognition of images with the use of highly accurate convolutional neural networks. The proposed system improves stages of normalization and segmentation of an image of the license plate, taking on large camera angles. The goal of improvements is to increase of accuracy of recognition. On the stage of normalization, before histogram equalization, the affine transformation of the image is performed. For the process of segmentation and recognition, Mask R-CNN is used. As the main segment-search algorithm, selective search is chosen. The combined loss function is used to fasten the process of training and classification of the network. The additional module to the convolutional neural network is added for solving the interclass segmentation. The input for this module is generated feature tensor. The output is segmented data for semantic processing. The developed system was compared to well-known systems (SeeAuto.USA and Nomeroff.Net). The invented system got better results on large camera shooting angles.

Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

The main challenge of automatic license plate recognition (ALPR) systems is that the overall performance is highly dependent upon the results of each component in the system’s pipeline. This paper proposes an improved ALPR system for the Jordanian license plates. Ceiling analysis is carried out to identify potential enhancements in each processing stage of a previously reported ALPR system. Based on the obtained ceiling analysis results, several enhancements are then suggested to improve the overall performance of the system under study. These improvements are (i) vertical-edge histogram analysis and size estimation of the candidate regions in the detection stage and (ii) de-rotation of the misaligned license plate images in the segmentation unit. These enhancements have resulted in significant improvements in the overall system performance despite a <1% increase in the execution time. The performance of the developed ALPR is assessed experimentally using a dataset of 500 images for parked and moving vehicles. The obtained results are found to be superior to those reported in equivalent systems, with a plate detection accuracy of 94.4%, character segmentation accuracy of 91.9%, and character recognition accuracy of 91.5%.

Recognition of Hybrid Graphic-Text License Plates

Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.