License plate identification and recognition in a non-standard environment using neural pattern matching

Abstract

Non-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

Automatic Jordanian License Plate Detection and Recognition System Using Deep Learning Techniques

“…Ref. [50] also applied YOLOv3 to recognise Pakistani LP, which achieved an accuracy of 96% for 571 plate character images. In [51], the YOLOv3 model was applied to recognise Iranian LPs, where the dataset used consisted of 5719 images, and the accuracy was 95.05%.…”

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

“…Clustering, similar to classification, identifies related data items; however, unlike classification, the class identities are unknown (like unsupervised learning) [11]. Cluster analysis is a common approach that is utilized not just in the field of data mining but also in statistics, segmentation of pictures, recognition of patterns, object recognition, retrieving data, computational biology, and other fields [19][20][21][22].…”

Vehicle detection system based on shape, color, and time-motion

“…In other case, non-standard license plate detection has been implemented in Pakistan. Private number plates should be recognized and monitored for several purposes including security as well as a well-developed traffic system [12]. Other algorithms such as single shot detection (SSD) algorithm can classify and detect real-time objects precisely.…”

“…For this reason, it is necessary to consider the optimal number of multimedia sensors to be used and the positioning of the multimedia sensors if the solution occurs in the event of a system failure [17], [18]. The methodology and achievement of a performance of this multimedia sensor installation are for visual information collection, identification, and tracking of vehicle plate numbers, and the effect of vehicle color on the recognition and tracking of vehicle plate numbers [12].…”