问题
I have a large collection of scanned images, and they are all somewhat skewed, with a white area around them.
So, these images have rectangles of colors, surrounded by a large white area. The problem is that these rectangles of color are not parallel to the image border.
I'm sure there must be a way to programmatically detect these rectangles of color, so that I can rotate the image (thus un-skewing it) and then crop it so that just the interesting part is left. I guess I'm not really sure what this process is called, so I am having trouble searching for a solution on Google.
Does anyone know of an approach that would get me started? Any libraries out there that I should look into? Or the name of an algorithm that would help?
I am planning on using Java for this project, but I haven't really started yet, so I am open to library suggestions in any language.
回答1:
- border detection
- hough transform (if all rectangles on an image have the same skew)
- rectangle contour detection (connected component contour, then minimum area bounding rectangle)
回答2:
Alyn is a third party package to detect and fix skew in images containing text. It uses Canny Edge Detection and Hough Transform to find skew.
To detect the skew, just run
./skew_detect.py -i image.jpg
To correct the skew, run
./deskew.py -i image.jpg -o skew_corrected_image.jpg
回答3:
You might also try scikit-image http://scikit-image.org/docs/dev/auto_examples/.
It's a great library for the hough transformation, but also has other methods like Radon transformation and geometric transformations for this kind of task.
This is a python library.
来源：https://stackoverflow.com/questions/3170501/how-can-i-detect-and-correct-skew-in-images