Affine Invariant Dynamic Time Warping and its Application to Online Rotated Handwriting Recognition

Dynamic time warping (DTW) has been widely used to align and compare two sequences. DTW can efficiently deal with local warp or deformation between sequences. However, it can't take account of affine transformation of sequences, such as rotation, shift and scale. This paper introduces a novel affine invariant dynamic time warping (AI-DTW) method, which tries to deal with the affine transformation and sequence alignment in a unified framework. We propose an iterative algorithm to estimate the optimal transformation matrix and warping path by mutually updating them. Recognition experiments on the online rotated handwritten data illustrated that the AI-DTW achieves a recognition rate of 95.54%, which is significantly higher than that (65.87%) of the classical DTW method

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