1. Project by Arie Kozak

3.  Mark it using personal biological visual system.

4.  Divide the image into two connected sub- images divided by red border.

5.  Use thresholding twice: after high pass and original image. Text found in the intersection.

6.  Constant albido assumption for ink, doesn’t work, use (cubic) interpolation.  Smooth image with Gaussian kernel before to reduce “sharpening effect” (lateral inhibition), and also after.

9.  Identify “clusters” – areas of local maxima/minima. All points within certain % of highest intensity values.

10.  Start with H = 0, perform for each cluster separately.

11.  Find closest clusters A and B; B with known height.  For points in A close to B, calculate expected height according to B.  Find closest points using Voronoi diagram.

14.  If v is new x-axis, calculate projection of all points to YZ plane.

15.  Use polyline approximation. Given number of desired points = number of clusters + 2, the desired error can be approximated using binary search.  Example – 5 points:

16.  Finally, use spline, on polyline edge points.

17.  Not perfect, usually works sufficiently.

19.  Detect sheet of paper automatically.  Relax assumptions (light direction, H is constant in one direction).  Improve clusters search.  Replace/improve polyline approximation.  Use this for text recognition.