Affine-invariant Recognition of Handwritten Characters via Accelerated KL Divergence Minimization

This paper proposes a new, affine-invariant image matching technique via accelerated KL (Kullback-Leibler) divergence minimization. First, we represent an image as a probability distribution by setting the sum of pixel values at one. Second, we introduce affine parameters into either of the two images' probability distributions using the Gaussian kernel density estimation. Finally, we determine optimal affine parameters that minimize KL divergence via an iterative method. In particular, without using such conventional nonlinear optimization techniques as the Levenberg-Marquardt method we devise an accelerated iterative method adapted to the KL divergence minimization problem through effective linear approximation. Recognition experiments using the handwritten numeral database IPTP CDROM1B show that the proposed method achieves a much higher recognition rate of 91.5% at suppressed computational cost than that of 83.7% obtained by a simple image matching method based on a normal KL divergence.