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

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

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

Detecting Vehicle Numbers Using Google Lens-Based ESP32CAM to Read Number Characters

plates continues to increase. This research aimed to detect vehicle license plates using ESP32CAM and utilize photo text reading using Google Lens, which can be used online to retrieve numeric characters. The method approach was to connect Wifi connectivity to the ESP32CAM, which had been programmed to detect vehicle plates. Vehicle plates that have been detected and recognized were inputted into Google Lens to capture the resulting text from the ESP32CAM camera recording. The results of this study were that for 70 seconds, ten plate samples were obtained, which were 100% perfect in reading license plates on Google Lens, namely six plates and two plates read 90%, one plate read 60%, and one plate read 0%. The research conclusions obtained were ten samples, six samples with perfect readings, and one error sample because of the white plate color. Thus, the main objective was to obtain the results of the vehicle plate detection and retrieve the text from the recording result

Super-resolução em vídeos de baixa qualidade para aplicações forenses, de vigilância e móveis

Orientadores: Siome Klein Goldenstein, Anderson de Rezende RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Algoritmos de super-resolução (SR) são métodos para obter um aumento da resolução de imagens compostas por pixels. Na super-resolução por múltiplas imagens, um conjunto de imagens de baixa resolução de uma cena é combinado para construir uma imagem de resolução superior. Super-resolução é uma solução barata para superar as limitações dos sistemas de aquisição de imagens, e pode ser útil em diversos casos em que o dispositivo não pode ser melhorado ou substituído - mas em que é possível obter diversas capturas da mesma cena. Neste trabalho, é explorada a super-resolução por múltiplas imagens para imagens naturais, em cenários nos quais é possível obter diversas imagens de uma cena. São propostas cinco variações de um método que explora propriedades geométricas de múltiplas imagens de baixa resolução para combiná-las em uma imagem de resolução superior; duas variações de um método que combina técnicas de inpainting e super-resolução; e mais três variações de um método que utiliza filtros adaptativos e regularização para resolver um problema de mínimos quadrados. Super-resolução por múltiplas imagens é possível quando existe movimento e informações não redundantes entre as imagens de baixa resolução. Entretanto, combiná-las em uma imagem de resolução superior pode não ser computacionalmente viável por técnicas complexas de super-resolução. A primeira aplicação dos métodos propostos é para um conjunto de imagens capturadas pelos dispositivos móveis mais recentes. Este tipo de ambiente requer algoritmos eficazes que sejam executados rapidamente e utilizando baixo consumo de memória. A segunda aplicação é na Ciência Forense. Câmeras de vigilância espalhadas pelas cidades poderiam fornecer dicas importantes para identificar um suspeito, por exemplo, em uma cena de crime. Entretanto, o reconhecimento dos caracteres de placas veiculares é especialmente difícil quando a resolução das imagens é baixa. Por isso, este trabalho também propõe um arcabouço que realiza a super-resolução de placas veiculares em vídeos reais de vigilância, capturados por câmeras de baixa qualidade e não projetadas especificamente para esta tarefa, ajudando o especialista forense a compreender um evento de interesse. O arcabouço realiza todas as etapas necessárias para rastrear, alinhar, reconstruir e reconhecer automaticamente os caracteres de uma placa suspeita. O usuário recebe, como saída, a imagem de alta resolução reconstruída, mais rica em detalhes, e também a sequência de caracteres reconhecida automaticamente nesta imagem. São apresentadas validações quantitativas e qualitativas dos algoritmos propostos e de suas aplicações. Os experimentos mostram, por exemplo, que é possível aumentar o número de caracteres reconhecidos corretamente, colocando o arcabouço proposto como uma ferramenta importante para fornecer aos peritos uma solução para o reconhecimento de placas veiculares sob condições adversas de aquisição. Por fim, também é sugerido o número mínimo de imagens a ser utilizada como entrada em cada aplicaçãoAbstract: Super-resolution (SR) algorithms are methods for achieving high-resolution (HR) enlargements of pixel-based images. In multi-frame super resolution, a set of low-resolution (LR) images of a scene are combined to construct an image with higher resolution. Super resolution is an inexpensive solution to overcome the limitations of image acquisition hardware systems, and can be useful in several cases in which the device cannot be upgraded or replaced, but multiple frames of the same scene can be obtained. In this work, we explore SR possibilities for natural images, in scenarios wherein we have multiple frames of a same scene. We design and develop five variations of an algorithm which rely on exploring geometric properties in order to combine pixels from LR observations into an HR grid; two variations of a method that combines inpainting techniques to multi-frame super resolution; and three variations of an algorithm that uses adaptive filtering and Tikhonov regularization to solve a least-square problem. Multi-frame super resolution is possible when there is motion and non-redundant information from LR observations. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. The first application of the proposed methods is in consumer-grade photography with a setup in which several low-resolution images gathered by recent mobile devices can be combined to create a much higher resolution image. Such always-on low-power environment requires effective high-performance algorithms, that run fastly and with a low-memory footprint. The second application is in Digital Forensic, with a setup in which low-quality surveillance cameras throughout the cities could provide important cues to identify a suspect vehicle, for example, in a crime scene. However, license-plate recognition is especially difficult under poor image resolutions. Hence, we design and develop a novel, free and open-source framework underpinned by SR and Automatic License-Plate Recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, super resolve, and recognize its alphanumerics. The user receives as outputs the rectified and super-resolved license-plate, richer in details, and also the sequence of license-plates characters that have been automatically recognized in the super-resolved image. We present quantitative and qualitative validations of the proposed algorithms and its applications. Our experiments show, for example, that SR can increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions. Finally, we also suggest a minimum number of images to use as input in each applicationDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação1197478,146886153996/3-2015CAPESCNP

Reconocimiento Automático de Matrículas de Automóviles Particulares Mexicanos con Información del Color

En este trabajo se presentan los resultados del reconocimiento de matriculas de autos mexicanos empleando visión artificial.El reconocimiento de las matrículas de vehículos ha sido investigado ampliamente en todo el mundo: Argentina, Bangladesh, China, Egipto, India, Japón, Malasia, entre otros. Normalmente los trabajos relacionados con ésta finalidad consisten en tres fases: 1) Localización de la placa dentro de la imagen, 2) extracción de los caracteres y 3) clasificación o reconocimiento de los caracteres. En el caso de México, el obstáculo principal se encuentra en la fase de extracción de caracteres, porque los algoritmos que existen en la literatura asumen que la placa no tiene patrones de textura en el fondo de la misma, debido a que en otros países por lo general el fondo es blanco y los caracteres negros. Sin embargo, en el caso de las placas mexicanas éstas tienen patrones de textura en el fondo produciendo que los algoritmos que funcionan bien para las placas sin patrones de textura no siempre funcionan correctamente. Por otro lado, cada entidad federativa y cada nuevo gobierno estatal puede diseñar su propio patrón de textura de fondo, esto implica que puedan existir por lo menos 32 clases de placas, número que se incrementa con los cambios en la administración gubernamental. Es importante mencionar que, si bien cada entidad federativa puede diseñar el fondo de sus placas, las dimensiones de las placas y letras así como su estilo deben cumplir con las características que señala la Norma Oficial Mexicana NOM-001-SCT-2-2000; estas características son las que se emplean para reconocer la matrícula. De aquí que se propone crear un algoritmo que segmente los caracteres y los reconozca en función de sus características de color y de forma. Para segmentar los caracteres de forma adecuada, se eliminó la mayor cantidad de los patrones de textura del fondo mediante el uso de un factor umbral, con el que se separaron los colores oscuros, que forman las letras; de los claros, que forman el fondo. Una vez filtrada la textura de fondo,la imagen de la placa se binarizó y se obtuvieron los histogramas horizontal y vertical mediante la técnica de proyección de perfiles, con la finalidad de obtener las coordenadas de posición que se utilizaron para segmentar los caracteres. Ya obtenidas las imágenes de los caracteres, se procedió a modelarlos y caracterizarlos mediante las técnicas de: momentos de Hu, Descriptores de Fourier y el Factor de Correlación Cruzada. Los datos obtenidos en esta etapa, se emplearon como alimentación en la etapa de clasificación. Finalmente, en la etapa de clasificación, se utilizaron las técnicas de Plantillas, Clasificador Bayesiano y Redes Neuronales Artificiales. Los resultados obtenidos se discuten al final del trabajo.Beca CONACyT para realizar estudios de maestría con el número de registro 561707