外文文献翻译

(含：英文原文及中文译文)

文献出处：Gao Q, Wang X, Xie G. License Plate Recognition Based On Prior Knowledge[C]// IEEE International Conference on Automation and Logistics. IEEE, 2007:2964-2968.

英文原文

License Plate Recognition Based On Prior Knowledge

Qian Gao, Xinnian Wang and Gongfu Xie

Abstract - In 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 the 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. 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 the license plates were degraded severely.

Index Terms - License plate recognition, prior knowledge, vehicle license plates, neural network.

I. INTRODUCTION

Vehicle 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 usi