我工作的车牌识别项目。所以，我目前所面临的问题是，我必须去偏移二进制图像中的字符，所以我可以提高我的项目模板匹配我的准确度。

I am working on number plate recognition project. So currently the problem I am facing is that I have to de-skew the characters in binary image so I can increase my accuracy of template matching in my project.

所以，我已经做了很多pre-处理，以消除图像的不必要的像素，我可以分段的人物了。但不幸的是它是歪斜。

So I have done a lot of pre-processing to remove unnecessary pixels of the image and I could segment the characters out. But unfortunately it is skew..

从...转换为灰度为二进制

From... converting to greyscale to binary

然后.. pre-处理技术..

Then.. pre-processing techniques..

分割后..

After segmentation..

正如你看到的最后一个图像，字符是扭曲的，这将导致不准确的模板匹配来进行识别的目的。

As you observed the last image, the characters are skewed and this will leads to inaccuracy for template matching to perform recognition purposes.

因此​​，任何人都可以教我如何解决这个问题去歪斜的图像从第二图像（后pre-处理）本身..请...大多数研究人员都在使用Hough变换进行偏移校正操作，但有没有其他更简单的方式做到这一点？

Therefore, can anyone teach me how to solve this problem to de-skew the image from 2nd image (after pre-processing) itself.. please... Most of the researchers are using Hough transform to perform the de-skew operation but is there other easier way to do this?

推荐答案

有办法对付这种

但你要unskew这样：

But you want to unskew so:

检测旋转角度/倾斜坡度 在获得边框 在投垂直扫描线，并记得先打点 在通过所有的人都回归行

旋转/通过它倾斜回来

现在的旋转/ unskew图像是静止倾斜芽多少率要低得多

[注意事项]