Research of Indonesian license plates recognition on moving vehicles

The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: detects moving vehicles, judges the existence of moving vehicles, captures moving vehicle images, deblurring images, locates license plates, extracts vertical edges, removes unnecessary edge lines, segments license plate locations, Indonesian license plate cutting character segmenting, character recognition. Experiments were carried out under several conditions: suitable conditions, poor lighting conditions (dawn, evening, and night), and unfavourable weather conditions (heavy rain, moderate rain, and light rain). In the experiment to test the success of the license plate number recognition, it was seen that the proposed method succeeded in recognizing 98.1 % of the total images tested. In unfavorable conditions such as poor lighting or when there are many disturbances such as rain, there is a decrease in the success rate of license plate recognition. Still, the proposed method's experimental results were higher than the method without deblurring by 1.7 %. There is still unsuccessful in recognizing license plates from the whole experiment due to a lot of noise. The noise can occur due to unfavourable environmental conditions such as heavy rain

Efficiently locating vehicle license plates based on vertical line detection

[[abstract]]A fast method for locating vehicle license plates is proposed in this paper. This approach is primarily based on the observations that the intensity contrast between license plate background color and symbol color is high and that symbol edges are clustered in the plate area. A simple edge detection method based on first derivatives is applied to a car image such that the binary image containing only vertical edges is obtained. Then possible plate regions could be located from the horizontal and vertical projections of the binary image. No preprocessing, such as image enhancement and noise filtering, is required. In addition to its simplicity and fastness, the proposed method is less sensitive to illumination changes of the plate. The proposed method was experimented with 117 diverse car images on a Pentium-133 processor. The system successfully located 116 license plates in 0.2 second on an average.[[notice]]補正完畢[[conferencetype]]國內[[conferencedate]]19990822~19990824[[conferencelocation]]臺北縣, 臺

Real-time Automatic License Plate Recognition Using Color Features

Various researchers presented various solutions for license plate detection but real-time performance is still a challenge in the field. In this paper, we propose a fast license plate detection method to work correctly in a real-time environment. In the first step, we locate or detect the license plate in the image sequences. We used color-based methods to detect the license plate. The method consists of computing image contours, later, we analyzed the contours to localize the license plate in the image sequences. After detecting the license plate, in the second step, we perform segmentation using a character recognition model. Finally, we propose the license plate format checking model to verify the detected license plate is the correct license plate. For the tools, we used OpenCV (open computer vision library) and tesseract for character recognition

IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work

MINHLP: Module to Identify New Hampshire License Plates

A license plate, referred to simply as a plate or vehicle registration plate, is a small plastic or metal plate attached to a motor vehicle for official identification purposes. Most governments require a registration plate to be attached to both the front and rear of a vehicle, although certain jurisdictions or vehicle types, such as motorcycles, require only one plate, which is usually attached to the rear of the vehicle. We present analysis of Automatic License Plate Recognition (ALPR) of New Hampshire (NH) plates using open source products. This thesis contains an implementation of a demonstrated model and analysis of the results. In this paper, OpenCV (computer vision library) and Tesseract (open source optical character reader) is presented as a core intelligent infrastructure. The thesis explains the mathematical principles and algorithms used for number plate detection, processes of proper characters segmentation, normalization and recognition. A description of the challenges involved in detecting and reading license plate in NH, previous studies done by others and the strategies adopted to solve them is also given