Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Encyclopedic dictionary of mathematics (2nd ed.)
Encyclopedic dictionary of mathematics (2nd ed.)
Using Generative Models for Handwritten Digit Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line, Handwritten Numeral Recognition by Perturbation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Cursive Kanji Character Recognition Using Stroke-Based Affine Transformation
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
Affine-Invariant Recognition of Gray-Scale Characters Using Global Affine Transformation Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Character Classification Using Nearest Neighbor in Large Databases
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]
Character Recognition by Affine Moment Invariants
CAIP '93 Proceedings of the 5th International Conference on Computer Analysis of Images and Patterns
Handwritten Character Recognition Using Piecewise Linear Two-Dimensional Warping
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A Survey of Elastic Matching Techniques for Handwritten Character Recognition
IEICE - Transactions on Information and Systems
Moment normalization of handprinted characters
IBM Journal of Research and Development
Toward affine recognition of handwritten mathematical characters
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Multi-template GAT/PAT Correlation for Character Recognition with a Limited Quantity of Data
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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This paper addresses the problem of reinforcing the ability of the k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although the k-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching in k-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.