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

Pemeriksa Jawaban Tulisan Tangan untuk Ujian Pilihan Ganda Menggunakan Hybrid Extreme Learning Convolutional Neural Network Machine

Di Indonesia, ujian dapat dilakukan dengan berbagai cara tergantung dengan tipe pelaksanaannya yaitu berupa Paper Based Test (PBT), Oral Based Test (OBT), dan Computer Based Test (CBT). Tipe yang paling sering digunakan di sekolah-sekolah adalah PBT yaitu berupa jawaban esay dan pilihan ganda. Namun beda halnya dengan tipe ujian pilihan ganda. Tipe ujian ini biasanya digunakan pada saat ujian kelulusan siswa atau yang lebih dikenal sebagai Ujian Nasional (UN). Dalam pelaksanaannya, UN menerapkan PBT dengan konsep soal pilihan ganda. PBT yang diterapkan pada UN menggunakan metode Object Character Recognition (OCR). Namun seiring berjalannya waktu terjadi evaluasi dari metode ini. Saat ini tipe ujian PBT mulai ditinggalkan dan beralih  ke tipe ujian CBT. Namun kedua tipe ini memiliki kekurangan dan kelebihan masing-masing. M elihat peluang tersebut, maka penelitian ini mengusulkan solusi baru dengan menggabungkan kelemahan dan kelebihan dari kedua tipe tersebut. Solusi yang diberikan adalah dengan memanfaatkan kecerdasan buatan seperti halnya OCR dengan mengusulkan metode baru yaitu Hybrid Extreme Convolutional Neural Network Machine

A GRAPHIC PROCESSING UNIT FRAME WORK FOR CONVOLUTIONAL NEURAL NETWORK BASED CLASSIFICATION OF REMOTELY SENSED SATELLITE IMAGES

Near real time processing and feature extraction from high-resolution satellite images aids in various applications of remote sensing including segmentation, classification and change detection. The latest generation of satellite sensors are able to capture the data at a very high spatial, spectral and temporal resolution. The processing time required for such a huge data is also large. Disaster monitoring applications such as forest fire monitoring, earthquakes require fast/real time processing of high resolution data to enable response activities. In general, due to the large size of satellite data, the computational time of feature calculation and training neural network is found to be very high. Therefore in order to achieve the aim of near real time processing of such huge data, we developed a parallel implementation. The implementation is performed on NVIDIA’s Graphical Processing Unit. The performance improvement obtained is demonstrated by a GPU implementation on Resourcesat-1 data and compared with the traditional sequential implementation. The results show that the GPU implementation is found to achieve performance improvement in terms of execution time and speedup throughput as compared to the sequential implementation

Intelligent System for Vehicles Number Plate Detection and Recognition Using Convolutional Neural Networks

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.</jats:p

Система розпізнавання архітектурних елементів на основі нейронних мереж

Бакалаврська робота містить оптимізацію задачі розпізнавання архітектурних елементів та формування наближеної вартості нерухомості. Розроблено алгоритм для підвищення точності розрахунків.Bachelor's work contains optimization of the task of recognizing architectural elements and forming the approximate cost of real estate. An algorithm is developed for increasing the accuracy of calculations.Бакалаврская работа содержит оптимизацию задачи распознавания архитектурных элементов и формирования приближенной стоимости недвижимости. Разработан алгоритм для повышения точности расчетов

Study of object detection and reading(license plate detection and reading)

Object detection means finding the location of the object and recognizing what it is. The techniques used for the object detection are feature matching algorithm, pattern comparison and boundary detection. The feature matching algorithm is used to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized. Our object detection is to detect the license plate detection of the car. To detect the license plate of a car first pre-process the image. The commonly license plate locating algorithms include line detection method, neural networks method, fuzzy logic vehicle license plate locating method. “Connected component analysis” is very easy technique than these techniques. In the pretreatment process we first crop the image. After this we convert the color image to gray level image. After converting into gray level that image is filtered using three different types of filters. They are Average, Median, Weiner filters. After deciding the good filter we will apply the segmentation process using edge detection. After finding the edges we will give the numbers to each connected component and store all the connected components in a matrix called labeling matrix. Extract the required connected component using the labeling matrix and store that in an image. Compare this template with our database using template matching technique. Template matching technique uses the correlation procedure. We will find the correlation coefficient between the two templates. Depending upon the correlation coefficient we will find that how much the two templates are similar to each other