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Shape Context - Rotation Invariance

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I was trying to implement Shape Context in order to identify a single textual character. I was trying to achieve rotation invariance.

The general approach for shape context is to compute distances and angles between each set of interest points in a given image. You then bin into a histogram based on whether these calculated values fall into certain ranges. You do this for both a standard and a test image. To match two different images, from this you use a chi-square function to estimate a "cost" between each possible pair of points in the two different histograms. Finally, you use an optimization technique such as the hungarian algorithm to find optimal assignments of points and then sum up the total cost, which will be lower for good matches.

I've checked several websites and papers, and they say that to make the above approach rotation invariant, you need to calculate each angle between each pair of points using the tangent vector as the x-axis. (ie http://www.cs.berkeley.edu/~malik/papers/BMP-shape.pdf page 513)

What exactly does this mean? No one seems to explain it clearly. Also, from which of each pair of points would you get the tangent vector - would you average the two?

A couple other people suggested I could use gradients (which are easy to find in toolsets such as Matlab) and use this as a substitute for the tangent points, though it does not seem to compute reasonable cost scores with this. Is it feasible to do this with gradients?

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