Robust search-free car number plate localization incorporating hierarchical saliency

There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1 -norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost

Retrieval of Anomaly Details Using Vehicle Number Plate Identification for Traffic Guards

The ascent in number of vehicles makes different issues in regular daily existence. Arranging such substantial number of vehicles and transportation are intricate and tedious assignment. This paper centers over the above issue. This framework will consequently perceive the number plate of vehicles. The perceived number plate takes after the given strides: 1.To catch continuous picture of number plate. 2. To fragment and perceive characters at the server. 3. Perceived tag is shown on the graphical UI and furthermore put away in database alongside time and date for further utilize. 4. Book the complaint against the anomaly. The different methodologies for the issue are contemplated as takes after

Incorporating negentropy in saliency-based search free car number plate localization

License plate localization algorithms aim to detect license plates within the scene. In this paper, a new algorithm is discussed where the necessary conditions are imposed into the saliency detection equations. Measures of distance between probability distributions such as negentropy finds the candidate license plates in the image and the Bayesian methodology exploits the a priori information to estimate the highest probability for each candidate. The proposed algorithm has been tested for three datasets, consisting of gray-scale and color images. A detection accuracy of 96% and an average execution time of 80 ms for the first dataset are the marked outcomes. The proposed method outperforms most of the state-of-the-art techniques and it is suitable to use in real-time ALPR applications

Real-time search-free multiple license plate recognition via likelihood estimation of saliency

In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency estimation. This method uses a new 3-D object tracking algorithm which includes: object detection, shadow detection and removal, and object recognition based on Bayesian methods. The algorithm is tested over three image datasets with different levels of complexities, and the results are compared with those of benchmark methods in terms of speed and accuracy. Unlike most search-based license-plate extraction methods, our proposed 3-D Bayesian saliency algorithm has lower execution time (less than 60 ms), more accuracy, and it is a search-free algorithm which works in noisy backgrounds

A design of license plate recognition system using convolutional neural network

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy

A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced

Artificial Neural Network-based Approach for Plate Segmentation and Character Recognition

A procedure is presented for Plate Segmentation and Character Recognition through artificial neural network (ANN). All the tasks are accomplished using following steps. Violation Detection, Violation Plate localization, and Plate Recognition. The neural network was able to learn the nonlinear relationship between the pixel ratios for each of the nine blocks and a unique character and are able to help us out In resolving Artificial Neural Network-based Approach for Plate Segmentation and Character Recognition proble

Automatic Number Plate Recognition using Random Forest Classifier

Automatic Number Plate Recognition System (ANPRS) is a mass surveillance embedded system that recognizes the number plate of the vehicle. This system is generally used for traffic management applications. It should be very efficient in detecting the number plate in noisy as well as in low illumination and also within required time frame. This paper proposes a number plate recognition method by processing vehicle's rear or front image. After image is captured, processing is divided into four steps which are Pre-Processing, Number plate localization, Character segmentation and Character recognition. Pre-Processing enhances the image for further processing, number plate localization extracts the number plate region from the image, character segmentation separates the individual characters from the extracted number plate and character recognition identifies the optical characters by using random forest classification algorithm. Experimental results reveal that the accuracy of this method is 90.9%