6,115 research outputs found
License plate localization based on statistical measures of license plate features
— License plate localization is considered as the most important part of license
plate recognition system. The high accuracy rate of license plate recognition is depended on
the ability of license plate detection. This paper presents a novel method for license plate
localization bases on license plate features. This proposed method consists of two main
processes. First, candidate regions extraction step, Sobel operator is applied to obtain
vertical edges and then potential candidate regions are extracted by deploying mathematical
morphology operations [5]. Last, license plate verification step, this step employs the
standard deviation of license plate features to confirm license plate position. The
experimental results show that the proposed method can achieve high quality license plate
localization results with high accuracy rate of 98.26 %
Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Benefiting from the rapid development of convolutional neural networks, the
performance of car license plate detection and recognition has been largely
improved. Nonetheless, challenges still exist especially for real-world
applications. In this paper, we present an efficient and accurate framework to
solve the license plate detection and recognition tasks simultaneously. It is a
lightweight and unified deep neural network, that can be optimized end-to-end
and work in real-time. Specifically, for unconstrained scenarios, an
anchor-free method is adopted to efficiently detect the bounding box and four
corners of a license plate, which are used to extract and rectify the target
region features. Then, a novel convolutional neural network branch is designed
to further extract features of characters without segmentation. Finally,
recognition task is treated as sequence labelling problems, which are solved by
Connectionist Temporal Classification (CTC) directly. Several public datasets
including images collected from different scenarios under various conditions
are chosen for evaluation. A large number of experiments indicate that the
proposed method significantly outperforms the previous state-of-the-art methods
in both speed and precision
IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS
Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work
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