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License Plate Recognition Based On Prior Knowledge(基于BP神经网络的车型识别)中英文翻译
湖南科技大学智能控制理论论文姓名:_____________学院:_____________班级:_____________学号:_____________License Plate Recognition Based On Prior KnowledgeAbstractIn this paper, a new algorithm based on improvedBP (back propagation) neural network for Chinese vehiclelicense plate recognition (LPR) is described. The proposedapproach provides a solution for the vehicle license plates (VLP)which were degraded severely. What it remarkably differs fromthe traditional methods is the application of prior knowledge oflicense plate to the procedure of location, segmentation andrecognition. Color collocation is used to locate the license plate inthe image. Dimensions of each character are constant, which isused to segment the character of VLPs. The Layout of theChinese VLP is an important feature, which is used to constructa classifier for recognizing. The experimental results show thatthe improved algorithm is effective under the condition that thelicense plates were degraded severelyVehicle License-Plate (VLP) recognition is a very interesting but difficult problem. It is important in a number of applications such as weight-and-speed-limit, red traffic infringement, road surveys and park security [1]. VLP recognition system consists of the plate location, the characters segmentation, and the characters recognition. These tasks become more sophisticated when dealing with plate images taken in various inclined angles or under various lighting, weather condition and cleanliness of the plate. Because this problem is usually used in real-time systems, it requires not only accuracy but also fast processing. Most existing VLP recognition methods [2], [3], [4], [5] reduce the complexity and increase the recognition rate by using some specific features of local VLPs and establishing some constrains on the position, distance from the camera to vehicles, and the inclined angles. In addition, neural network was used to increase the recognition rate [6], [7] but the traditional recognition methods seldom consider
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