Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine-Invariant Recognition of Gray-Scale Characters Using Global Affine Transformation Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Handwritten Character Recognition Using Piecewise Linear Two-Dimensional Warping
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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This paper addresses the problem of how to construct a robust character classifier when statistical pattern recognition techniques fail because of a limited quantity of data. The key ideas are two ways. One is to adopt a distortion-tolerant shape matching technique. Here, we use an affine-invariant matching technique of global affine transformation (GAT) correlation to absorb linear distortion between grayscale images. The other is to prepare multiple templates for dealing with nonlinear distortion or topologically different shapes. For this purpose K-means clustering is applied to a given limited data in a simple gradient feature space. Recognition experiments using the handwritten numeral database IPTP CDROM1B show that the proposed method achieves a much higher recognition rate of 97.2% as compared to that of 85.8% obtained by the conventional, simple correlation matching with a single template per category.