Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5 over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2x faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.
 In this work, we tackle the problem of car license plate detection and recognition in natural scene images. Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition. Firstly, we train a @math -class convolutional neural network (CNN) to detect all characters in an image, which results in a high recall, compared with conventional approaches such as training a binary text non-text classifier. False positives are then eliminated by the second plate non-plate CNN classifier. Bounding box refinement is then carried out based on the edge information of the license plates, in order to improve the intersection-over-union (IoU) ratio. The proposed cascade framework extracts license plates effectively with both high recall and precision. Last, we propose to recognize the license characters as a sequence labelling problem. A recurrent neural network (RNN) with long short-term memory (LSTM) is trained to recognize the sequential features extracted from the whole license plate via CNNs. The main advantage of this approach is that it is segmentation free. By exploring context information and avoiding errors caused by segmentation, the RNN method performs better than a baseline method of combining segmentation and deep CNN classification; and achieves state-of-the-art recognition accuracy. Registration plate recognition is widely used in detecting speedy cars, traffic law enforcement and electronic toll collection. The problems associated with registration plate recognition are, plate images have different quality, illumination, view angle, distance, complex background and fonts. To address these problems, image processing tools are used for extracting only the region of interest. Then different luminance and background are removed by changing colored image to grayscale and then in to binary matrix form. Character recognition algorithm is applied on the binary images. All components of image are compared with pre-defined standard template and characters are recognized based on best match. Each matched character is displayed and stored in a log file. This algorithm is tested with vehicle images of different backgrounds and illumination. The camera focus, viewing plane and the distance from the vehicle were varied. The results of experiments are fascinating with good accuracy rate. In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5 ). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1 . The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0 . A review in the related literature presented in this paper reveals that better performance (90 up to 95 ) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low  This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments. Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness. An algorithm for license plate recognition (LPR) applied to the intelligent transportation system is proposed on the basis of a novel shadow removal technique and character recognition algorithms. This paper has two major contributions. One contribution is a new binary method, i.e., the shadow removal method, which is based on the improved Bernsen algorithm combined with the Gaussian filter. Our second contribution is a character recognition algorithm known as support vector machine (SVM) integration. In SVM integration, character features are extracted from the elastic mesh, and the entire address character string is taken as the object of study, as opposed to a single character. This paper also presents improved techniques for image tilt correction and image gray enhancement. Our algorithm is robust to the variance of illumination, view angle, position, size, and color of the license plates when working in a complex environment. The algorithm was tested with 9026 images, such as natural-scene vehicle images using different backgrounds and ambient illumination particularly for low-resolution images. The license plates were properly located and segmented as 97.16 and 98.34 , respectively. The optical character recognition system is the SVM integration with different character features, whose performance for numerals, Kana, and address recognition reached 99.5 , 98.6 , and 97.8 , respectively. Combining the preceding tests, the overall performance of success for the license plate achieves 93.54 when the system is used for LPR in various complex conditions. License plate localization (LPL) and character segmentation (CS) play key roles in the license plate (LP) recognition system. In this paper, we dedicate ourselves to these two issues. In LPL, histogram equalization is employed to solve the low-contrast and dynamic-range problems; the texture properties, e.g., aspect ratio, and color similarity are used to locate the LP; and the Hough transform is adopted to correct the rotation problem. In CS, the hybrid binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms. This work proposes a novel adaptive approach for character segmentation and feature vector extraction from seriously degraded images. An algorithm based on the histogram automatically detects fragments and merges these fragments before segmenting the fragmented characters. A morphological thickening algorithm automatically locates reference lines for separating the overlapped characters. A morphological thinning algorithm and the segmentation cost calculation automatically determine the baseline for segmenting the connected characters. Basically, our approach can detect fragmented, overlapped, or connected character and adaptively apply for one of three algorithms without manual fine-tuning. Seriously degraded images as license plate images taken from real world are used in the experiments to evaluate the robustness, the flexibility and the effectiveness of our approach. The system approach output data as feature vectors keep useful information more accurately to be used as input data in an automatic pattern recognition system.