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字符识别

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This paper mainly researches on character segmentation and recognition techniques which are used in license plate recognition system.

文章研究了车牌识别系统中的字符分割和识别技术。

Some experiments of handwriting capital English letter recognition is taken and better result is obtained.

本文以手写体大写英文字符的识别为例介绍该方法,通过实验证明该方法具有较好的识别效果。

Integral geometry is applied here to picture analysis and recognition.

本文应用积分几何于图象分析与识别之中,引进了Crofton整体参量和方向参量C, CD/J其中,D——广义方向直径,J为方向投影宽度,提出了一种称之为Crofton字符几何识别方案。

Final design of the park vehicle management system and neural network recognition of license plate image from the identification, park vehicles in order to carry out effective management.

最后设计了园区车辆管理系统,并用神经网络的识别方法对所得车牌字符图像进行识别,从而对园区车辆进行有效的管理。

For the automatic recognition of these characters have become an important part of pattern recognition.

对这些字符的自动识别已成为模式识别的一项重要内容。

Digital authentication image for the object image extraction method of character analysis and research, achieve code characters in the picture extraction and recognition.

详细说明:以数字验证图片为对象进行图像的分析与字符提取的方法研究,实现对验证码图片中字符的提取与识别。

Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%; and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively.

通过20个实际交叉口场景测试数据验证,采用所提出的方法,停车线检测正确率达90%,信号灯检测正确率为85%,在信号灯字符正确分割出来的基础上,字符的识别率达97%。

This dissertation brings forward and realizes the multilevel classifiable method which is based on characters coding. Above all, this method realizes the first-grade classification by extracting enough effective characters from characters and coding them, to the others which couldn't be recognized by the first-grade classification, the method will adopt the second-grade classification using template matching to recognize these characters.

本文使用了基于特征编码的多级分类识别方法,通过给字符抽取足够多的有效的特征并给特征编码实现第一级分类,对于第一级分类后仍不能区分的字符,再进入第二级分类用模板匹配的方法最终达到区分的目的,这种方法的重点在第一级分类阶段。

This dissertation brings forward and realizes the multilevel classifiable method which is based on characters coding. Above all, this method realizes the first-grade classification by extracting enough effective characters from characters and coding them, to the others which coundn t be recognized by the first-grade classification, the method will adopt the second-grade classification using template matching to recognize these characters.

本文提出并实现了基于特征编码的多级分类识别方法,通过给字符抽取足够多的有效的特征并给特征编码实现第一级分类,对于第一级分类后仍不能区分的字符,再进入第二级分类用模板匹配的方法最终达到区分的目的,这种方法的重点在第一级分类阶段。

Under the circumstances of being training sets, we use the rough reseau to extract features of characters.

在有训练样本集合的情况下,采用粗网格方法提取字符的特征,利用粗糙集在属性特征约简方面的优势,对冗余特征进行有效删减,压缩了字符的特征数目,提高了识别效率。

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