A Review of Automatic License Plate Recognition System in Mobile based Platform

Automatic license plate recognition (ALPR) is the process of retrieving license plate information from a captured image or video frames from a sequence of videos. ALPR can assist law enforcement officers to identify stolen vehicles or to capture vehicle information from those that violate traffic laws instantly. It is also commonly used as an electronic payment system for toll payment or parking fee payment. Traditionally, ALPR is installed in a PC-based platform to take advantage of its processing power to process high-quality images captured by high-resolution cameras. Most smartphones nowadays are equipped with a high-quality camera and faster processing system which can be used to develop portable ALPR system. Thus, this has encouraged many researchers to work on implementing ALPR technology for the mobile platform. In this paper, we reviewed several researches that have implemented ALPR in the mobile-based platform. We discuss the techniques used in the three main stages of ALPR namely localisation, segmentation and recognition. The advantages and disadvantages of each technique are summarised in a table

A Real-Time Mobile Vehicle License Plate Detection and Recognition

100學年度研究獎補助論文[[abstract]]In this paper we present a instant and real-time mobile vehicle license plate recognition system in an open environment. Using a nonfixed video camera installed in the car, the system tries to capture the image of the car in front and to process instant vehicle license plate detection and recognition. We utilize the color characteristics of the barking lights to carry out license plate detection. We first detect the location of the two barking lights in the captured image. Then set license plate detection region using the probability distribution of the license plate between the two lights. This method can eliminate any environmental interference during the license plate detection and improve the rate of accuracy of license plate detection and recognition. Moreover, we use the morphology method Black Top-Hat to enhance the level of separation of the license plate characters. Experiments show that the system can effectively and quickly capture the vehicle image, detect and recognize the license plate whether it is in daytime, nighttime, clear day, raining day or under complicated environment.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Automatic Vehicle Number Plate Recognition for Vehicle Parking Management System

A license plate recognition (LPR) system is one type of intelligent transportation system (ITS). It is a type of technology in which the software enables computer system to read automatically the license number plate of vehicle from digital pictures. Reading automatically the number plate means converting the pixel information of digital image into the ASCII text of the number plate. This paper discuses a method for the vehicle number plate recognition from the image using mathematical morphological operations. The main objective is to use different morphological operations in such a way that the number plate of vehicle can be identified accurately. This is based on various operation such as image enhancement, morphological transformation, edge detection and extraction of number plate from vehicle image. After this segmentation is applied to recognize the characters present on number plate using template matching. This algorithm can recognize number plate quickly and accurately from the vehicles image. Keywords: ANPR, ITS, Image Enhancement, Edge Detection, Morphological Operation, Number Plate Extraction,  Template Matching

Multiple License Plate Detection for Complex Background

[[abstract]]This paper presents a wavelet transfonn based method for extracting license plates from cluttered images. The proposed system consists of three major stages. First, a wavelet transfonn based method is used for extracting important contrast features as guides to search for desired license plates. Then, finding a reference line in HL subimage plays an important role to locate the desired license plate region roughly. According to the reference line we can decrease the searching region of license plate and speed up the execution time. The last stage of the method is to locate the license plate accurately by license plate adjustment. More importantly, the proposed detection method can locate multiple plates with different orientations in one image. Since the feature extracted is robust to complex backgrounds, the proposed method works well in extracting differently illuminated and oriented license plates. The average accuracy of detection is 92.4%.[[sponsorship]]IEEE Computer Society Technical Committee on Distributed Processing (TCDP); Tamkung University[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20050328~20050330[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺北縣, 臺

A Real-time Mobile Vehicle License Plate Detection and Recognition for vehicle monitoring and management

[[abstract]]In this paper we present a instant and real-time mobile vehicle license plate recognition system in an open environment. Using a nonfixed video camera installed in the car, the system tries to capture the image of the car in front and to process instant vehicle license plate detection and recognition. Relying on the instant vehicle body recognition, the system can detect and locate the vehicle license plate without the need of background image. Vehicle body detection system utilizes the color characteristics of the barking lights to carry out detection. It first detects the location of the two barking lights in the captured image. Then set license plate detection region using the probability distribution of the license plate between the two lights, thus quickly locate the license plate. This method can eliminate any environmental interference during the license plate detection. From the results of experiment, it is determined that this system can effectively and quickly capture the vehicle image, detect and recognize the license plate whether it is dark, raining or under complicated environments.[[sponsorship]]IEEE Taipei Section; National Science Council; Ministry of Education; Tamkang University; Asia University; Providence University; The University of Aizu; Lanzhou University[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20091203~20091205[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Tamsui, Taipei, Taiwa

Real-Time Vision System for License Plate Detection and Recognition on FPGA

Rapid development of the Field Programmable Gate Array (FPGA) offers an alternative way to provide acceleration for computationally intensive tasks such as digital signal and image processing. Its ability to perform parallel processing shows the potential in implementing a high speed vision system. Out of numerous applications of computer vision, this paper focuses on the hardware implementation of one that is commercially known as Automatic Number Plate Recognition (ANPR).Morphological operations and Optical Character Recognition (OCR) algorithms have been implemented on a Xilinx Zynq-7000 All-Programmable SoC to realize the functions of an ANPR system. Test results have shown that the designed and implemented processing pipeline that consumed 63 % of the logic resources is capable of delivering the results with relatively low error rate. Most importantly, the computation time satisfies the real-time requirement for many ANPR applications

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