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We propose a stroke classification method based on affine alignment, appropriate for online recognition of mathematical handwriting. The method, essentially linear is simple and computationally efficient. The modeling limitations of the affine group are overcome by choosing adequate error functions and by performing alignment with respect to interpolated prototypes. So, moderate nonlinear transformations are tolerated, making the approach invariant to a wide range of handwriting deformations.