Malaysian Vehicle License Plate Recognition Using Double Edge Detection

Vehicle plate number is a unique combination of characters and numbers. Hence, it has been used in various application as personal identification such as for parking system identification, security monitoring system and etc. This paper illustrated the double edge detection technique in order to enhance the vehicle plate image, before character recognition process. Firstly, the vehicle image is captured, and then it will be re-sized and cropped until the resolution of image is 300×300. After the re-sized process, First Edge detection is applied to the image. Threshold of black and white are 59 and 60 respectively used to change the image into black and white colour only. Next, Second Edge detection is used to remove the unwanted image and only remain the plate number in white colour. MATLAB software is used in this experiment

Malaysian vehicle license plate recognition using deep learning and computer vision

License plate recognition has become one of the popular topics under deep learning researches. There are many deep learning models and the suitable model for this project chose according to the ability to meet the system operation requirements such as speed, accuracy and precision of the outcome. Therefore, YOLO (You Only Look Once) model was used which is fast in processing the more images and produce the output at a single look. YOLO is an algorithm designed for multi object detection in a single neural network where it only sees once and process to detect object as many as possible in a picture. In this paper, YOLOv3 is use to detect the position of car registration plate. Next, image warping and slicing applied to straighten the image so it will be easy to feed into character recognition process. Then, the PyTesseract will be used to read the characters from the image together with RegEx function to eliminate the weak predictions from the PyTesseract results. The results obtained from this approach achieved 100% accuracy in recognizing vehicle car plate from 5 video collected from Universiti Malaysia Pahang (UMP) main entrance security gate CCTV system

Automatic Vehicle Identification System Using License Plate

This paper presents a new approach for development of an Automatic Vehicle Identification System (A VIS). The proposed system can be divided into three major modules; they are vehicle image preprocessing, license plate feature extraction and classification algorithm based on Hidden Markov Model (HMM). Experiment has been conducted to demonstrate the effectiveness of the proposed system. The proposed system is tested using Nigeria vehicle license plates. Recognition rate of 98% is obtained; the result is superior in comparison with the results obtained from previous system

Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition

Model pengecaman corak memainkan peranan yang penting dalam banyak aplikasi dunia sebenar seperti pengesanan teks dan pengecaman objek. Pelbagai kaedah termasuk model Kecerdikan Berkomputer (CI) telah dibangunkan untuk menangani masalah pengecaman corak berasaskan imej. Tertumpu kepada model CI, penyelidikan ini mempersembah model berasaskan pengoptimuman kawanan zarah (PSO) yang cekap serta aplikasinya untuk pengecaman lesen plat. Pertama, model Pengoptimuman Kawanan Zarah Memetik berasaskan pengukuhan pembelajaran yang baharu (RLMPSO) diperkenalkan. Masalah pengoptimuman penanda aras digunakan untuk menilai prestasi RLMPSO, dan kaedah bootstarp digunakan untuk menilai keputusan secara statistik. Kedua, RLMPSO disepadukan dengan mesin Penyokong Vektor Kabur (FSVM) untuk merumuskan model RLMPSO-FSVM yang cekap. Secara khusus, RLMPSO-FSVM terdiri daripada gabungan pengelas linear FSVM yang dibina menggunakan RLMPSO untuk melaksanakan penalaan parameter, pemilihan ciri, serta pemilihan contoh latihan. Untuk menilai prestasi model RLMPSO-FSVM yang dicadangkan, pangkalan data imej penanda aras digunakan. Ketiga, model dua-peringkat RLMPSO-FSVM dicipta untuk mempertingkatkan lagi kecekapan. Ia mengandungi peringkat pengecaman global dan peringkat pengesahan tempatan. Peningkatan model RLMPSO turut diperkenalkan dengan memasukkan operasi carian tambahan. Model RLMPSO yang (ERLMPSO) dipertingkatkan terdiri daripada tiga lapisan, iaitu lapisan global dengan empat operasi carian, lapisan tempatan dengan satu operasi carian, dan lapisan berasaskan komponen dengan dua belas operasi carian. Akhir sekali, model dua-peringkat ERLMPSO-FSVM yang dicadangkan telah digunapakai dalam masalah Pengecaman Plat Lesen Kereta Malaysia (VLPR) yang sebenar. Kadar pengecaman setinggi 98.1% telah diperoleh. Keputusan ini mengesahkan keberkesanan model dua-peringkat ERLMPSO-FSVM yang dicadangkan dalam menangani masalah pengecaman plat lesen. ________________________________________________________________________________________________________________________ Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the literature to tackle image-based pattern recognition problems. Focused on CI models, this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly, a new Reinforcement Learningbased Memetic Particle Swarm Optimization (RLMPSO) model is introduced. To assess the performance of RLMPSO, benchmark optimization problems are employed, and the bootstrap method is used to quantify the results statistically. Secondly, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient RLMPSO-FSVM model. Specifically, RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning, feature selection, as well as training sample selection. To evaluate the performance of the proposed RLMPSO-FSVM model, a benchmark image database is employed. Thirdly, to further improve efficiency, a two-stage RLMPSO-FSVM model is devised. It consists of a global recognition stage and a local verification stage. In addition, enhancement of the RLMPSO model is introduced by incorporating additional search operations. The enhanced RLMPSO model (i.e. ERLMPSO) comprises three layers, namely, a global layer with four search operations, a local layer with one search operation, and a component-based layer with twelve search operations. Finally, the proposed two-stage ERLMPSOFSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task. A high recognition rate of 98.1% has been achieved, confirming the effectiveness of the proposed two-stage ERLMPSO-FSVM model in tackling the license plate recognition problem

A design of license plate recognition system using convolutional neural network

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy

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

PALESTINE AUTOMOTIVE LICENSE IDENTITY RECOGNITION FOR INTELLIGENT PARKING SYSTEM

Providing employees with protection and security is one of the key concerns of any organization. This goal can be implemented mainly by managing and protecting employees’ cars in the parking area. Therefore, a parking area must be managed and organized with smart technologies and tools that can be applied and integrated in an intelligent parking system. This paper presents the tools based on image recognition technology that can be used to effectively control various parts of a parking system. An intelligent automotive parking system is effectively implemented by integrating image processing technologies and an Arduino controller. Results show that intelligent parking is successfully implemented based on car ID image capture to meet the need for managing and organizing car parking systems

Character recognition based on region pixel concentration for license plate identification

Budući da je postalo moguće izvoditi digitalnu obradu slike u kratkom vremenu, njezina uporaba u tehničkim sustavima postaje sve češća. Automatsko prepoznavanje registarskih oznaka jedan je takav primjer. Koristeći digitalnu obradu slike moguće je automatski detektirati i prepoznati znakove s registarske oznake vozila. Koraci u ovom procesu su predprocesiranje slike, detekcija pločice, segmentacija i prepoznavanje znakova. Ovaj proces obavlja algoritam koji uzima digitalne slike kao ulaz i daje tekstualni oblik znakova s registarskih oznaka kao izlaz. Postoji nekoliko metoda koje se koriste za izvođenje ovog procesa. Ove metode su objašnjene u ovom radu i jedna od njih je implementirana u C# programskom jeziku. Rezultati pokazuju da algoritam radi dobro u slučajevima bez puno deformacija ulazne slike. Međutim, još uvijek postoje slučajevi u kojima nepredvidiva priroda ulazne slike može uzrokovati neuspješnu detekciju ili prepoznavanje.Since it has become possible to perform digital image processing in a short period of time, its usage in technical systems is getting more common. Automatic license plate recognition is one such example. By using digital image processing it is possible to automatically detect and recognize characters on vehicle license plates. The steps taken in this process are image preprocessing, plate detection, character segmentation and recognition. This process is performed by the algorithm which takes the digital image as an input and gives textual form of license plate characters as an output. There are several methods used to perform this process. These methods are explained in this paper and one of them is implemented in C# programming language. The results show that the algorithm works fine in cases without much deformation of an input image. However, there are still cases where unpredictable nature of an input image can cause unsuccessful detection or recognition