Edge-Based Licence-Plate Template Matching for Identifying Similar Vehicles

This paper presents licence-plate recognition for identifying vehicles with similar licence-plates. The method uses a modified licence-plate recognition pipeline, with licence-plate template matching replacing character segmentation and recognition. Only edge detection is used, combined with a method for calculating line ratio to locate and extract licence-plates. The extracted licence-plate templates are then compared for licence-plate matching. The results show that the method performs well in differing circumstances, and that it is computationally cost-effective. Results also show that licence-plate template matching is a reliable method of identifying similar vehicles, and has a lower computational cost when compared with character segmentation and recognition.

Comparative Analysis of Various Soft Computing Technique-Based Automatic Licence Plate Recognition Systems

Traffic on Indian roads is growing day by day leading to accidents. The intelligent transport system is the solution to resolve the traffic problem on roads. One of the components of the intelligent transportation system is the monitoring of traffic by the automatic licence plate recognition system. In this chapter, a automatic licence plate recognition systems based on soft computing techniques is presented. Images of Indian vehicle licence plates are used as the dataset. Firstly, the licence plate region is extracted from the captured image, and thereafter, the characters are segmented. Then features are extracted from the segmented characters which are used for the recognition purpose. Furthermore, artificial neural network, support vector machine, and convolutional neural network are used and compared for the automatic licence plate recognition. The future scope is the hybrid technique solution to the problem.

License plate recognition for campus auto-gate system

<p><span>Automatic licence plate recognition (LPR) has been a subject of study for the last few decades. Considering the recent advancements in machine learning methods and portable devices, this increasingly attracting researchers’ interest to provide more reliable LPR systems. Several LPR techniques have been reported in the literature in different intelligent transportation applications and surveillance systems, and yet a ropust LPR system remains a challenging research task. Because the performance of current techniques is subject to several factors and local conditions, this paper aims to explore the use of LPR in a specific application; i.e. Automatic plate recognition to monitor the entry and exit of vehicles at the university campus gates. Implementing an auto-gate system is an important application for a smooth control of flowing traffic especially during peak hours. We propose an automated system with the ability to capture, verify and recognize the license plates using image processing-based techniques. The system is aimed to work alongside existing access cards and other gate remote controls. Experimental evaluation of the system reveals a detection accuracy of 91.58%, a successful plate number segmentation rate of 91% and 80% accuracy of plate recognition.</span></p>

A Hybrid Approach For Automatic Licence Plate Recognition System

: Automatic licence plate recognition systems are used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. In this paper, a convolutional neural network and support vector machine based automatic licence plate recognition system is proposed. Firstly, The characters extracts from the input image of vehicle. Then characters are segment and their features are extracts. The extracted features are classified using convolutional neural network and support vector machine for the final recognition of the licence plate. The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The recognition rate obtained for the proposed hybrid automatic licence plate system are compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. The proposed automatic licence plate recognition system perform better than the neural network, support vector machine, and convolutional nerural network based automatic licence plate recognition systems.

Soft Computing Techniques Based Automatic Licence Plate Recognition Systems for Indian Vehicles

Automobile industries are growing exponentially in last decade in India. Growth in the vehicle numbers results in much more road accidents and traffic management problem. Not only this, long queues at toll plazas and parking lot is also a major issue of concern. Problem of traffic management and long queues can be solved by automatic licence plate recognition systems. In this paper, an automatic Licence Plate Recognition Systems based on soft computing techniques are presented. Indian vehicle with licence plates were used for testing the implemented systems. Firstly the licence plate image is extracted from the vehicle image and the characters are segmented from the extracted licence plate image and then features are extracted from the segmented characters which are used for the recognition. Soft computing techniques random forest, neural network, support vector machine, and convolutional neural network are used for the implementation pusrpose. The results obtained for the applied soft computing technique are compared to the last. The future scope is the hybrid technique solution to the problem