Abstract
This paper addresses the problem of improving the accuracy of character recognition with a limited quantity of data. The key ideas are twofold. One is distortion-tolerant template matching via hierarchical global/partial affine transformation (GAT/PAT) correlation to absorb both linear and nonlinear distortions in a parametric manner. The other is use of multiple templates per category obtained by k-means clustering in a gradient feature space for dealing with topological distortion. Recognition experiments using the handwritten numerical database IPTP CDROM1B show that the proposed method achieves a much higher recognition rate of 97.9% than that of 85.8% obtained by the conventional, simple correlation matching with a single template per category. Furthermore, comparative experiments show that the k-NN classification using the tangent distance and the GAT correlation technique achieves recognition rates of 97.5% and 98.7%, respectively.
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