Deployment of automatic licence plate recognition systems in multinational environments

: 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.

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

Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3299-7.ch002 ◽

2021 ◽

pp. 18-37

Author(s):

Nitin Sharma ◽

Pawan Kumar Dahiya ◽

B. R. Marwah

Keyword(s):

Neural Network ◽

Soft Computing ◽

Intelligent Transportation System ◽

Recognition System ◽

Support Vector ◽

Intelligent Transport System ◽

Hybrid Technique ◽

Network Support ◽

Recognition Systems ◽

Licence Plate Recognition

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.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3344.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 470-474

Keyword(s):

Neural Network ◽

Soft Computing ◽

Traffic Management ◽

Support Vector ◽

Hybrid Technique ◽

Management Problem ◽

Computing Technique ◽

Soft Computing Techniques ◽

Recognition Systems ◽

Licence Plate Recognition

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

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Human Ear Pattern Recognition System

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.35 ◽

2017 ◽

Vol 7 (8) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

V. Jagan Naveen ◽

K. Krishna Kishore ◽

P. Rajesh Kumar

Keyword(s):

Pattern Recognition ◽

Expectation Maximization Algorithm ◽

Recognition System ◽

Modern World ◽

Person Identification ◽

Biometric Systems ◽

Different Types ◽

Recognition Systems ◽

Human Ear ◽

Pca Algorithm

In the modern world, human recognition systems play an important role to improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm. Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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Facial recognition technologies and the new physiognomic era

Kritische Lebenskunst - psychosozial ◽

10.30820/0171-3434-2020-2-45 ◽

2020 ◽

Vol 43 (2) ◽

pp. 45-56

Author(s):

Abigail Nieves Delgado

Keyword(s):

Facial Recognition ◽

Personal Identification ◽

Digital Photographs ◽

Different Types ◽

Recognition Systems ◽

The Way

The current overproduction of images of faces in digital photographs and videos, and the widespread use of facial recognition technologies have important effects on the way we understand ourselves and others. This is because facial recognition technologies create new circulation pathways of images that transform portraits and photographs into material for potential personal identification. In other words, different types of images of faces become available to the scrutiny of facial recognition technologies. In these new circulation pathways, images are continually shared between many different actors who use (or abuse) them for different purposes. Besides this distribution of images, the categorization practices involved in the development and use of facial recognition systems reinvigorate physiognomic assumptions and judgments (e.g., about beauty, race, dangerousness). They constitute the framework through which faces are interpreted. This paper shows that, because of this procedure, facial recognition technologies introduce new and far-reaching »facialization« processes, which reiterate old discriminatory practices.

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