In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.
Several authors aimed to adapt general blind deconvolution methods to license plates. Fang @cite_2 proposed a new regularization term based on intensities and gradients. Song @cite_13 fuse L0-regularized deblurring with character recognition. Hsieh @cite_16 skip deconvolution and rather focus on recognition of blurred characters.
@cite_16 @cite_13 @cite_2
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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. 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. 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 Nowadays, drive recorders are becoming a popular form of evidences used by drivers and accepted by court. One common investigation task is to identify vehicles of interest and recognize their license plates (LPs). In this paper, we focus on License Plate Recognition (LPR) based on single snapshot from a drive recorder. As drive recorders are installed on moving vehicles, snapshots by drive recorders usually suffer from serious blur, and the key issue is recognizing the Blurred License Plate (BLP) from single image. A straightforward method is first deblurring the BLP and then recognizing it. However, the first problem with this method is that general image deblurring methods are designed to get a good overall visual effect and the deblurred results may be not good for LPR. The second problem is that general image deblurring methods don't use the features of the LPs, which could be important priors for the deblurring process. To overcome these issues, this paper proposes a novel method that integrates deblurring and recognizing in a closed-loop. The proposed method utilizes characters and patterns of LPs as priors, and the deblurring and recognizing process will stop when a reliable recognition result is obtained from the deblurred image. Furthermore, by analyzing the features of BLPs, this paper proposes a l 0 -norm based deblurring method. Experiments show that, compared to other LPR methods, the proposed method can achieve higher recognition rate on the BLPs.