AbstractExtracting of car license p

Abstract
Extracting of car license plate is important for identifying the car. Since there are some problems such as poor ambient lighting problem , bad weather problem and so on , the car images are distorted and the car license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method , some features of car license plate according to motor vehicle regulation such as color information , shape are applied to determine the candidates of car license plates. To certify the license plate , the characters , numbers and their patterns are recognized by backpropagation neural networks in windows which are opened in those boundaries of candidates. For the results of recognition by neural networks , the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. Since the results of characters-pattern recognition is used to certify the license plate , the ability of license-plate extracting is enhanced and the car is identified simultaneously. The results of the experiments with 70 samples of real car images show the performance of car license-plate extraction by 84.29% , and the recognition rate is 80.81%.

結果 (中文) 1:

抽象
提取的车牌是重要的是确定这辆车。因为，所以有一些问题，可怜的环境照明问题，恶劣的天气问题，汽车图像被扭曲和汽车车牌很难被提取。本文提出的提取车车牌使用机动车调控的方法。在此方法中，根据机动车辆规例的颜色信息，形状如汽车车牌的一些功能可应用于确定候选人的汽车牌照。证明车牌的字符、 数字和它们的模式被认可的反向传播神经网络在这些边界的候选人中打开的窗口中。用神经网络识别的结果，为候选人，已根据机动车规例的字符和数字模式作为车牌区域认证。由于字符模式识别的结果用来证明车牌，增强的车牌提取能力和这辆车同时确定。实验的结果与真车图象的 70 样本 84.29%，显示性能的车车牌提取和识别率是 80.81%。

結果 (中文) 3:

摘要
车牌提取识别汽车是重要的。因为有一些问题，如环境照明差的问题，恶劣的天气问题等等，汽车扭曲图像和车牌是很难被提取。本文提出了一种提取车牌使用机动车管理方法。在该方法中，一些特征的汽车车牌根据机动车的调控等形状颜色信息，用于确定车牌的候选人。证明车牌字符，数字，和他们的模式是公认的反向传播神经网络，在这些边界的候选人打开的窗口。对神经网络识别的结果，候选字符和数字模式，根据机动车辆条例注册为车牌区域。由于使用的字符模式识别的结果证明车牌，车牌提取的能力提高和汽车同时识别。70真正的汽车图像样本的实验结果表明的车牌提取84.29%的性能，且识别率是80.81%。