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

Feature extraction for license plate location based on L0-norm smoothing

We propose a simple feature extraction algorithm for license plate location, which can reduce the occurrence of pseudo-licenses significantly. Our scheme arises from a novel L-0 -norm image smoothing, in which the multiple local textures in the complex backgrounds can be suppressed remarkably without changing the structures and edges of the license objects. Due to this "edgeaware" property, we then combine a feature filtering with an efficient binarized image, a simple multi-scale image analysis algorithm, to remove the potential false license plates. Finally, we extract license plates with a projection method. Experimental results show the proposed method provides a flexible and powerful way to the license plate location in complex backgrounds

An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition

Vehicle Number Plate Recognition with Bilinear Interpolation and Plotting Horizontal and Vertical Edge Processing Histogram with Sound Signals

The Vehicle Number Plate Recognition is a system designed to help in recognition of number plates of vehicles. This type of system is designed for the objective of the security system. Vehicle Number Plate Recognition is based on the Image Processing system. Vehicle Number Plate Recognition helps in the functions like detection of the number plates of the car, processing them and using processed data for further processes like storing. The system is simulated and implemented in MATLAB, and its performance is tested on the real image. It is assumed that images of the vehicle have been captured from Digital Camera or Mobile Phones. Alphanumeric Characters on the plate has been extracted using the Template Images of Alphanumeric characters. Many times it becomes very difficult to identify the owner of the Vehicle who violates the traffic rules and drives the vehicle so fast. Therefore, it is difficult to catch and punish those people because the traffic personal might not be able to retrieve the vehicle number from the moving vehicle because of fast speed of the vehicle. Therefore, there is a need to develop Vehicle Number Plate Recognition (VNPR) system as this is one of the best solution to this problem

Smart License Plate Recognition Using Optical Character Recognition Based on the Multicopter

In recent years Unmanned Aerial Vehicle (UAV) is major focused of active research, since they can extend our capabilities in a variety of areas, especially for application like research detection, tracking and recognition. For our project goals is vehicle tracking and plate recognition. In addition, we have to combine some intelligence algorithms. In this project to define the number and type of vehicles, using our nation's roadways is becoming more and more important. This project used for Multicopter. The multicopter to flying around of the roadway. Because it is to collect roadway’s data. That means, to send a picture of a vehicle violating the law. Then our algorithm is recognizing to the number plate. In addition, this algorithm saving the vehicle number plate. We are great database in this algorithm. In this paper, template matching algorithm for character recognition is used. The developed system first detects the vehicle and capture the image. Then vehicle number plate region is extracted using the image segmentation in an image. Character recognition algorithm working on the OCR algorithm. We are detection accuracy to increase by using some algorithms. We combined these different algorithms using a modified version of PCA and OCR recognizer, we designed the proposed an architecture using OpenCV and we used to implement the design in the Multicopter

OCR Applied for Identification of Vehicles with Irregular Documentation Using IoT

Given the lack of investments in surveillance in remote places, this paper presents a prototype that identifies vehicles in irregular conditions, notifying a group of people, such as a network of neighbors, through a low-cost embedded system based on the Internet of things (IoT). The developed prototype allows the visualization of the location, date and time of the event, and vehicle information such as license plate, make, model, color, city, state, passenger capacity and restrictions. It also offers a responsive interface in two languages: Portuguese and English. The proposed device addresses technical concepts pertinent to image processing such as binarization, analysis of possible characters on the plate, plate border location, perspective transformation, character segmentation, optical character recognition (OCR) and post-processing. The embedded system is based on a Raspberry having support to GPS, solar panels, communication via 3G modem, wi-fi, camera and motion sensors. Tests were performed regarding the vehicle’s positioning and the percentage of assertiveness in image processing, where the vehicles are at different angles, speeds and distances. The prototype can be a viable alternative because the results were satisfactory concerning the recognition of the license plates, mobility and autonomy