In this paper, a method based on deep learning is presented to highlight and recognize the Iranian License plate numbers. The current research uses the convolutional neural network with the encoder-decoder structure to enhance the image and highlight the plate image numbers instead of using traditional image enhancement techniques. The proposed network can highlight vehicle License plate numbers by learning the plate images in various conditions. After that, the plate numbers are recognized from the reproduced image using a recurrent neural network without the need to plate image segmentation. This method can reduce the error caused by the License plate number segmentation. The proposed method reached the final recognition rate up to 94٫ 19 percent on a database with 4000 test images for recognizing the License plates which is acceptable in comparison to three recent methods.

Car License plate recognition is addressed in this paper. Given the development of intelligent transportation systems, it is absolutely essential to implement a strong License plate recognition system. Efforts were made to put forward a novel reliable method for car License plate recognition in Iran. Each License plate recognition system comprises three main parts. The first part is the License plate detection stage. The blue color feature of the License plate margin along with Scale-Invariant Feature Transform (SIFT) algorithm were used for this purpose. The accuracy of the presented method over the database was approximately 90% in less than a second. License plate morphological features were utilized upon character segmentation. Using these features, areas with sizes close to that of the characters of a License plate may be searched. The accuracy of this method was almost 95%. A probabilistic neural network together with a Support Vector Machine (SVM) was employed at the character recognition stage. For this stage, an accuracy of nearly 97% in 55 milliseconds for each License plate was achieved.

According to today's statistics, more than half a billion vehicles move around the world, making inspection and monitoring one of the basic needs of any traffic control system. All vehicles have an identification number exhibited as the License plate, their primary ID, a vital element. Deep Learning methods are adopted to detect vehicle License plates. This proposed method consists of two steps: highlighting the License plate and reading the ID stages. In this context, the combination of deep neural networks (DNN) and the competitive generative adversarial network (GAN) is applied in the encoding-coder network/structure for this highlighting. The proposed models are assessed based on the FZU Cars and Stanford Cars datasets, to which the results of this study are compared and discussed. The findings here indicate that the accuracy of this proposed model is almost 98%, subject to the two datasets.

License plate recognition is one of the most important applications in intelligent transportation systems. Difficulty of correct detection and identification of the car plates in different environment conditions makes researchers try new approaches to better solve the problem. License plate recognition problem is divided into three sub problems: "plate Location", "Character Segmentation", and "Character Identification". In this paper we have tried to improve location and identification of Iranian License plate with fuzzy rules. License locating has been done with edge detection, morphological operations and using fuzzy rules and characters have been identified by fuzzy support vector machine. By applying the algorithm on 50 images, 90% of plates were located and 94% of characters were identified successfully. This shows superiority of our algorithm over non-fuzzy approaches.

This paper presents a new classification framework for Iranian License plate character recognition. In this framework, a set of robust features are calculated from License plate characters based on directional projections, kirsch edge detector and local means. The characters are then classified using mixture of experts which use the multilayer Perceptrons (MLPs) as expert and gating networks. The proposed recognition algorithm is evaluated on a database of Iranian License plate characters consisting of 14256 binary images, and the recognition rate of 99.42% is achieved. The proposed algorithm yields better performance of the Iranian License plate character recognition in comparison with conventional methods which use a single MLP neural network.

According to today's statistics, more than half a billion vehicles are moving in the world and inspection and monitoring is one of the basic needs of any traffic system. All cars have an identification number or the same License plate as their primary ID, which today is one of the most suitable vehicle authentication tools. In this paper, the high capacity of deep neural networks in learning License plate identifiers is used. The proposed model of this paper has two stages of highlighting the License plate and reading the ID. In this regard, for highlighting, the combination of YOLO and XGBOOST network is used in encoder-coder network. The proposed model is evaluated on the FZU Cars dataset and based on the results of the experiments, the proposed model has a higher accuracy than the basic methods.

1. Introduction As human populations and the number of vehicles are growing rapidly, it appears that vehicles need to be automatically detected. The License plate number is a unique identifier used to authenticate an automobile's identity. Applying image processing on still images or video footages taken by color, black-and-white, and infrared cameras, automatic License plate recognition systems play an important role in constructing smart parking lots, counting the number of cars, monitoring the speed of cars, maintaining roads, monitoring traffic crimes, etc. The License plate recognition (LPR) process basically consists of three general steps: locating the License plate, separating the characters, and identifying the License plate characters. locating the License plate is one of the most important and challenging steps in the automatic License plate recognition process. In this stage, if the License plate is not located in a short time with acceptable accuracy, the overall time taken by the License plate reader system will increase, making other separation and detection steps inefficient. There are a number of challenges in this regard, including the diversity of License plate shapes, uneven lighting conditions, License plate angles, distance of cameras from cars, reflection and refraction of light in the imaging process, low image quality, and time-consuming process of developing a relevant algorithm. Accordingly, it could be said that there is no single definitive solution for detecting License plates. 2. Theoretical Principles Edge detection is a widely used method in locating the License plate, using a combined algorithm based on statistical analysis and morphology of vertical edges. Some studies have applied a two-step process in which information on vertical image edge as a horizontal representation of the frequency band was used to detect the candidate areas and the exact location of the License plate. Moreover, the studies statistics published by South Khorasan Province's Cultural Heritage Office, Tourism Organization, Medical Sciences University, 4700 tourists have visited this province in the second half of the year 2017, out of whom some 354 people were selected as the study's sample size using available sampling. The study's findings revealed that most of the tourists visited South Khorasan Province's health tourism attractions individually or in friendly groups, and few people visited the areas with their families. Moreover, the results of the analysis of the data collected by the questionnaires showed that most of the tourists stayed in those areas for less than a day. Therefore, it appears that the best way to get acquainted with and informed of South Khorasan Province's Herbal medicine and medical herbs' attractions and opportunities is to compare the experiences of those who visited health tourism attractions with those who visited other types of tourism areas. 4. Conclusion The results of investigating the status of health tourism in Iran show that the country enjoys many strengths in terms of this type of tourism, including qualified physicians, cutting-edge technology, and natural treatment areas that can attract health tourists. However, it faces some weaknesses and challenges such as inappropriate coordination between the organizations responsible for medical tourism and inadequate planning. Therefore, Iran can enhance its status in the health tourism market by investing more in its strengths and introducing them as unique capabilities in terms of offering services, and overcoming its weaknesses by developing comprehensive plans, marketing, and correcting some processes. In fact, if health tourism and medicinal plants' technology are well-organized, they can attract more tourists and consequently enhance the level of public knowledge and awareness, improve the society's health, increase the prosperity of medicinal plants' and Herbal medicine's market, improve the economic growth rate, and increase the export of medicinal plants both domestically and abroad. Possessing a variety of natural attractions (parks, forests, mountains, rivers, etc. ) and appropriate climate conditions, South Khorasan province can well contribute to Iran's health tourism if relevant organizations undertake to organize hot and mineral springs and improve their conditions, and reduce medical costs. Moreover, paying attention to factors such as investments in infrastructure and human resources to strengthen Herbal Medicine and create relevant databases, facilitating organizations in tourism management, health services, natural resources, cultural heritage, and information technologies can significantly contribute to strengthening tourism.

In this paper a License plate detection and recognition system for Iranian private cars is implemented. The proposed License plate localization algorithm is based on region elements analysis which works properly independent of distance (how far a vehicle is), rotation (angle between camera and vehicle), and contrast (being dirty, reflected, or deformed).In addition, more than one car may exist in the image. The proposed method extracts edges and then determines the candidate regions by applying window movement. The region elements analysis includes binarization, character analysis, character continuity analysis and character parallelism analysis. After detecting License plates, we estimate the rotation angle and try to compensate it. In order to identify a detected plate, every character should be recognized.For this purpose, we present 25 features and use them as the input to an artificial neural network classifier. The experimental results show that our proposed method achieves appropriate performance for both detection and recognition of the Iranian License plates.