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The license plate in the image is not only rotated but also skewed.

The skewing transformation matrix has the following form

[ 1  sy  0 ]
[ sx  1  0 ]

In your case, sy=-0.3, sx=0 (approximative value). You have to transform the rotated image with this matrix, or multiply the rotation and the skewing matrix and use warpAffine with the combined matrix.

To get a robust solution, I suggest to detect the four corners of the license plate and determine directly the affine transformation matrix using the findHomography function.

The license plate in the image is not only rotated but also skewed.

The skewing transformation matrix has the following form

[ 1  sy  0 ]
[ sx  1  0 ]

In your case, sy=-0.3, sx=0 (approximative value). You have to transform the rotated image with this matrix, or multiply the rotation and the skewing matrix and use warpAffine with the combined matrix.

To get a robust solution, I suggest to detect the four corners of the license plate and determine directly the affine transformation matrix using the findHomography function.

Mat skew_mat = Mat::eye(2,3,CV_64F);skew_mat.at<double>(0,1)=-0.3;skew_mat.at<double>(1,0)=0;
Mat skewed;
warpAffine(rotated, skewed, skew_mat, rotated.size());