License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques

With the increasing advancement of science and technology, the popularity of automobiles in my country has gradually increased. Among them, license plate positioning and license plate recognition technology are very important key technologies in the license plate recognition system. Through the license plate positioning technology, the position information, centroid and other data information of the license plate can be obtained, so as to realize the positioning of the license plate. The license plate recognition technology can extract the uniqueness of the vehicle. Identifying the license plate number, so the design and implementation of vehicle recognition and license plate recognition technology based on embedded devices have practical significance and practical application value to promote the technological progress of intelligent transportation and vehicle recognition systems. This design is a license plate recognition system, an embedded character segmentation recognition processing platform with ARM Cortex-A9 as the core, and the method of cross-compiling OpenCV, Qt, BootLoader in the Linux operating system, and transplanting related drivers to the ARM Cortex-A9 platform; Based on the classic algorithm, the OpenCV library function is used to realize the segmentation and recognition of the license plate image characters. The experimental results show that the design has achieved the expected goal.

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

This paper presents a robust real time feature based algorithm for localization and recognition of multiple color and font style license plates. The algorithm uses digitized license plate images for robust detection and recognition. The resolution of the captured image is adjusted and the brightness and contrast are set to appropriate levels. An optimal threshold is applied to detect white regions of the enhanced image. The algorithm uses segmentation and component labeling to create candidate regions intelligently. For localization of the license plate, features of each region are extracted and analyzed by rectangularity percentage of the automobile's license plate. The use of color filter renders the algorithm more robust on license plate localization. Moreover, we use HSV color space to find the specific color regions. After localization of license plate in small sized image, the proposed algorithm uses rapid intelligent method for localization of license plate form the original image. The localized license plate image is cropped and processed by the recognition algorithm. Each character's region is identified by using segmentation algorithm based on the profile which includes character dilation and resizing for subsequent operations. In this paper, correlation optical character recognition and multi-layer perception neural networks techniques are used for recognizing the characters. The performance of the proposed algorithm is acceptable even in low-quality or bad light images.

In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.

As the difference of gray value between the numbers and letters on the license plate and the license plate background is very big. We first use region growing algorithm based on the gray value to separate each number and letter into different area. Secondly, we search for the bounding rectangles of all regions and find some rectangular sequences that meet a special condition. Thirdly, calculate the horizon bias for all rectangles according to the coordination of the up-right point in the rectangle, adjust and get the precise location of each rectangle. Forth, we get the binary image which can separate the numbers and letters on the license plates and the license plate background by the dynamic threshold algorithm. The relation of the maximum width of various license plate numbers and letters and its bounding rectangles helps to count the direction of the vertical bias, then the bias is adjusted. At last we statistic the foreground vertical histogram of the previous results, and get the result of precise vertical position and character segmentation. The experimental results show that this algorithm performs well for location of multi-national license plates.

In this paper, we present a robust Chinese license plate recognition system. The system consists of three modules: license plate localization, character segmentation and character recognition. In the license plate localization module, Color point pairs are searched to produce the plate region candidates coarsely, and the shape, texture features are used to refine the candidate. In character segmentation module, skew Correction, Character Segmentation and Thinning Processing is employed to improve the segmentation performance. In the character recognition module, SVM classifiers are used to generate effective result. Experimental result shows that License plate localization achieved accuracy of 94.19% and character recognition 94.51%.

The intent of this research is to design a license plate recognition (LPR) system in the domain of Bangla language for smart vehicle management. The proposed system is designed on the basis of computer vision tools and deep supervised machine learning model. The system has three modules: license plate detection, character segmentation and recognition of the characters of the License Plate (LP). The goal of detection is to localize the plate area from the vehicle image and to crop region of interest (LP). It is executed by applying following process: preprocessing the image, conversion to binary image, contour detection and filtering the contours to get the LP's character contours, tilt correction and cropping the plate area from the image. Then, the cropped LP is segmented to extract the characters from the plate. Finally, the recognition step classifies the characters by means of deep convolution neural network where the features of the character are crafted and learned by the convolution layers of the networks. The system is implemented in Python OpenCV environment for offline car license plates images which are taken in different illuminations, road scenarios and colored cars. The system performance is evaluated in terms of detection rate, segmentation rate, recognition rate and execution time. The results illustrate that the performance of the system is remarkable.

Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.

License plate segmentation is a key technology in the process of license plate location and recognition. How to realize automatic segmentation of license plate image under complex illumination conditions has been a hot issue in intelligent transportation system (ITS). This paper deals with license plate image segmentation under a variety of lighting conditions. Based on the adaptive segmentation of license plate images by the Pulse Coupled Neural Network (PCNN), the relationship between the license plate image contrast and the PCNN iteration entropy is analyzed. An adaptive segmentation algorithm for license plate image using Deep Neural Network (DNN) to select the optimal result is proposed, and the selected segmentation image is filtered by the connected domain, which lays a foundation for subsequent license plate location, character segmentation and recognition. Simulation experiments show that the proposed algorithm performs better license plate segmentation and optimal selection for license plate images under various lighting conditions.

Image processing-based vehicle recognition is one of the important research fields in ITS. The existing methods are all based on license plate recognition and car shape recognition. Their common problem is algorithm stability. And the license plates are easy to be changed. All information about vehicles should be used to recognize them reliably. A problem to be solved is to find a method to recognize vehicles besides license plate recognition and vehicle model recognition. Vehicle license plate location and character segmentation are critical steps in the license plate recognition system, and yet there are difficult problems to be solved. Kernel density estimation and Mean Shift theory

The License plate location is an important stage in Intelligent Transportation System (ITS). It is considered to be the most crucial step of an Automatic License Plate Recognition (ALPR) system. A new image segmentation method based on integrated features and Marker-Controlled Watershed algorithm was proposed to locate and split the plate region of vehicle images from complicated environment. Firstly, the color license plate image was converted to grayscale image, and Top-hat transform was applied to restrain background and eliminate variant illumination to enhance the contrast of the image, and then Marker-Controlled Watershed algorithm was used to segment the preprocessed images to get candidate regions, lastly, a new distinguishing method based on integrated features of the plate was proposed to locate the accurate plate region. Experimental results show that this new method is simple, effective and robust to interference characters and complicated background.

In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images taken in bad weather and nighttime. In this paper, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection. Then, we apply convolution neural networks to improve the character recognition of blurred and obscure images. The experimental results show the superiority of the performance in both accuracy and performance in comparison with traditional license plate recognition systems.

This paper presents an efficient method for license plate detection and recognition that will aid in the development of an intelligent transport system. To significantly accelerate license plate localization a simple image pre-processing method is first used without reducing detection performance compared with that obtained using original image. Line density filter approach is used to extract the license plate regions, there by significantly reducing area to be analysed for license plate localization. In license plate recognition, the characters are recognized from these detected license plates by using optical character recognition and the owner details are displayed. The aim of this paper is to serve the purpose of accurately localizing vehicle license plates from complex scenes in real time that would help motor vehicle department in various ways.

Traffic control and vehicle owner identification become major problems in Bangladesh. Most of the time it is difficult to identify the driver or the owner of the vehicles who violate the traffic rules or do any accidental work on the road. Moreover, it is very time-consuming for a traffic police officer to physically check the license plate of every vehicle. So, an automatic license plate recognition system is a much-needed solution to solve these problems. The existing Bangla license plate recognition systems are mostly based on character segmentation and these methods are not implemented in real-time. In this study, two separate Deep Convolutional Neural Network (DCNN) models are used to identify the license plate and the characters on the license plate from the real-time video streaming. The first CNN model detects the license plate from the live video of a vehicle on the road. Than it crop the license plate area from the video frames. The cropped frame is then fed into the second CNN to detect the characters on that license plate. The characters are detected as individual objects. After detecting all the characters and numbers on the license plate, they are rearranged according to their position on the plate. To train the proposed model total of 292 images are collected used. Moreover, an open-sourced Bangla handwritten character dataset named BanglaLekha-Isolated is also used to train the model with synthetic character data. The trained model is tested using 18 live videos and 6 still image data. Finally, the proposed methodology gains a 100% precision on detecting the license plate, and 91.67% precision for detecting the characters on the license plate for the given test dataset.

Automatic license plate recognition is a technology that relies on optical character recognition methods used on images for reading license plate text. These techniques are, in general, used with image processing techniques, whether the system is composed of them entirely or in addition to deep learning techniques and neural network models. Such a system can be used by CCTV cameras, smart cameras, and other such devices, for tasks in areas like law enforcement, toll collection systems, or parking lot ticket systems. These systems can be used to capture images, locate license plates and recognize the text from the license plate. Depending on the region, post-processing can be conducted to further refine the recognized text based on a set of predefined rules, such as the position of letters and numbers in the license plate string. This work aims to create an automatic license plate recognition system with the use of image processing techniques, mainly from the OpenCV python library. The system will attempt to recognize license plate text from vehicles parked in parking lots. This can be useful for people, who do not remember the place where they parked their vehicle, or in other areas such as law enforcement. For large parking lots, there are generally camera systems already in place, in this case, the system should be able to work with multiple angles and views of the parking lot to get results with the highest accuracy ratings.