How can I detect (and correct) skew in images?

Question

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.

Solution 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)

Solution 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

Solution 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.

Comments

[liran]

Thanks! A search for Hough Transform led me to recognition-software.com/image/deskew which didn't solve my problem right out of the box, but I was able to tweak the code a bit to get it to work very well.

[wprl]

Was it subsumed by Tess4j?