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1、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 improved BP (back propagation) neural network for Chinese vehicle license plate recognition (LPR) is described. The proposed approach provides a solution for th
2、e vehicle license plates (VLP) which were degraded severely. What it remarkably differs from the traditional methods is the application of prior knowledge of license plate to the procedure of location, segmentation and recognition. Color collocation is used to locate the license plate in the image.
3、Dimensions of each character are constant, which is used to segment the character of VLPs. The Layout of the Chinese VLP is an important feature, which is used to construct a classifier for recognizing. The experimental results show that the improved algorithm is effective under the condition that t
4、he license 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
5、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-t
6、ime 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 ve
7、hicles, and the inclined angles. In addition, neural network was used to increase the recognition rate 6, 7 but the traditional recognition methods seldom consider the prior knowledge of the local VLPs. In this paper, we proposed a new improved learning method of BP algorithm based on specific featu
8、res of Chinese VLPs. The proposed algorithm overcomes the low speed convergence of BP neural network 8 and remarkable increases the recognition rate especially under the condition that the license plate images were degrade severely.Index Terms - License plate recognition, prior knowledge, vehicle li
9、cense plates, neural network.1. Neural Network Introduction ObjectiveAs you read these words you are using a complex biological neural network. You have a highly interconnected set of some 1011 neurons to facilitate your reading, breathing, motion and thinking. Each of your biological neurons，a rich
10、 assembly of tissue and chemistry, has the complexity, if not the speed, of a microprocessor. Some of your neural structure was with you at birth. Other parts have been established by experience.Scientists have only just begun to understand how biological neural networks operate. It is generally und
11、erstood that all biological neural functions, including memory, are stored in the neurons and in the connections between them. Learning is viewed as the establishment of new connections between neurons or the modification of existing connections.This leads to the following question: Although we have
12、 only a rudimentary understanding of biological neural networks, is it possible to construct a small set of simple artificial “neurons” and perhaps train them to serve a useful function? The answer is “yes.”This book, then, is about artificial neural networks.The neurons that we consider here are no
13、t biological. They are extremely simple abstractions of biological neurons, realized as elements in a program or perhaps as circuits made of silicon. Networks of these artificial neurons do not have a fraction of the power of the human brain, but they can be trained to perform useful functions. This
14、 book is about such neurons, the networks that contain them and their training. HistoryThe history of artificial neural networks is filled with colorful, creative individuals from many different fields, many of whom struggled for decades to develop concepts that we now take for granted. This history
15、 has been documented by various authors. One particularly interesting book is Neurocomputing: Foundations of Research by John Anderson and Edward Rosenfeld. They have collected and edited a set of some 43 papers of special historical interest. Each paper is preceded by an introduction that puts the
16、paper in historical perspective.Histories of some of the main neural network contributors are included at the beginning of various chapters throughout this text and will not be repeated here. However, it seems appropriate to give a brief overview, a sample of the major developments.At least two ingredi
