车牌

车牌定位在车牌识别系统（licenseplaterecognition，LPR）的实现过程中是一种极为重要的技术。

The location of license plate is a very important technology in the Vehicle License Plate Recognition ( LPR )?

提出一种反应不同模块特征的KC复杂性特征对所关心的车牌区域和背景模块进行标测，实现车牌定位。

We distinguish the license plate region and the background region with KC complexity .

另一名司机看清了该车的车牌号。

Another driver managed to get the registration number of the car .

他开一辆个性车牌的劳斯莱斯。

He drove a Rolls-Royce with a personalised number plate .

能清楚地看到一名警察记下了经过车辆的车牌号。

One policeman was clearly visible noting the number plates of passing cars

他目睹了枪击过程并记住了刺客的车牌号码。

He saw the shooting and memorised the number of the assassin 's car .

所以，许多中国人花了很多钱让自己的电话号码和车牌号里有“8”这个数字。

So , many Chinese people spend a lot of money to get their telephone numbers or car numbers to include this number " 8 " .

司机由于使用假车牌而被捕。

The driver was arrested for having false license plates on his car .

嵌入式LED光源在图象抓拍中的应用&提高车牌自动识别率的新途径

The Application of Built-in LED Light Source to Imagery Recognition

在车辆监控中应用基于DSP的车牌识别的研究

Research of DSP-based Vehicle License Number Plate Recognition Applied in Vehicle Monitor System

适于DSP实时实现的车牌自动识别技术研究

Research on Automatic Vehicle License Plate Recognition Adaptive for Real-time Implement by DSP

基于DSP芯片的车牌识别系统

License Plate Recognition System Based on DSP Chip

基于边缘与SVM的车牌自动定位与提取

Automatic License Plate Location and Extraction Based on Edge and SVM

基于SVM的矿用地磅车牌识别系统的研究

SVM-based Mining Loadometer License Plate Recognition System

研究了模板匹配、神经网络和SVM等几种常见的车牌字符识别算法，在此基础上提出了一种融合模板匹配和SVM的字符识别方法。

Several character recognition methods such as template matching , neural network and SVM are studied .

同时，将车牌识别算法移植到嵌入式DSP平台上，实现具有车牌识别功能的智能系统。

At the same time , the LPR algorithm was transplanted to the DSP platform and an intelligent system was implemented .

Gabor变换的参数设计及其在车牌字符识别中的应用

Gabor parameter design its use in character recognition of vehicle registration number

基于HSV颜色空间的一种车牌定位和分割方法

Approach for Locating and Segmenting Vehicle License Plates Based on HSV Color Space

关于车牌号OD调查方法的探讨

Discussion About Method of OD ( Origin-Destination ) Investigation of Vehicle License Plate Number

基于Canny边缘检测的车牌字符分割方法

A Plate Character Segmentation Method Based on Canny Edge Detection

基于小波包和Zernike矩特征提取的车牌字符识别

Vehicle license plate characters recognition using wavelet packet and zernike moments

提出了一种将边缘与SVM相结合的车牌定位与提取的方法。

In this paper , a new method of license plate location and extraction based on edge and SVM is presented .

并对该系统进行代码优化、存储器优化等，使得DSP车牌识别软件运行效率更高、识别速度更快。

And the system code optimization , memory optimization , makes the LPR system based on DSP chip to run more efficient and faster recognition speed .

其中包括异步串行通信模块、CRC校验模块、车牌中与基站同步的模块等等。

The serial communication module , CRC checksum generation module and synchronization module are discussed in detail .

车牌识别技术（VehicleLicensePlateRecognition）综合了计算机视觉技术和模式识别技术，在智能交通系统（IntelligentTrafficSystem，简称ITS）中占有重要的地位。

Vehicle License Plate Recognition ( LPR ) is a combination of the computer vision technology and the pattern recognition technology , and it plays an important role in Intelligent Traffic System ( ITS ) .

同时本文的文法在Matlab环境下，平均处理时间在0.8s/幅左右，较好的保证了车牌的实时性。

In the Matlab environment , with the grammar of this article , the average processing time in the 0.8s / pieces around the plate to ensure a better real-time .

首先介绍了几种典型的细化算法，以Zhang细化算法为基础，并加以适当改进，然后应用到车牌识别系统中。

A few of classic thinning algorithms are presented . An improved thing algorithm based on in Zhang thinning algorithm has been used to the car plate recognition system .

基于Top-hat变换的车牌快速搜索与定位

Fast search and location of license plates based on the Top-hat transform

本文着重对基于BP神经网络的字符识别方法进行了研究，并分析了其学习训练流程，并采用此方法实现了车牌字符的识别。

This article focuses on the character recognition method based on BP neural network , and analyze its learning and training process , and using this method to achieve license plate character recognition .

为了提高车牌上的字符识别准确率，提出一种结合支撑矢量机（SVM）和小波的字符识别方法。

To improve the recognition accuracy of the characters on car license plate , a novel method based on support vector machine ( SVM ) and wavelet decomposition was proposed .