In this section, let's look at the steps we will take to achieve our results. Our first task is to detect the paper from the background. For this, we will apply the k-means algorithm with two cluster centers. With the two cluster centers, we can detect which one of them represents the page and which one corresponds to the background, and create a binary image.

Now, we will use the cluster representing the paper and try to remove some noise and fill in some gaps with morphological opening and closing using a rectangular kernel.

Next, we will try to find the outer boundary of the page and use it to detect the corners. For this, we will detect the contours in the binary image and then identify the contour with the largest area.

Once we have the largest contour, we will detect the lines using a probabilistic Hough transformation. Then, we will join the lines and detect the corners.

Once we have the corners, we will detect which corner corresponds to which other corner, and then apply...