Aspect Ratio Adaptive Normalization for Handwritten Character Recognition
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Handwritten Chinese Character Recognition: Alternatives to Nonlinear Normalization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A Majority Voting Scheme for Multiresolution Recognition of Handprinted Numerals
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
The Neural-based Segmentation of Cursive Words using Enhanced Heuristics
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Handwritten Chinese character recognition: effects of shape normalization and feature extraction
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
International Journal of Applied Mathematics and Computer Science
k-NN classification of handwritten characters via accelerated GAT correlation
Pattern Recognition
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Handprinted characters can be made more uniform in appearance than the as-written version if an appropriate linear transformation is performed on each input pattern. The transformation can be implemented electronically by programming a flying-spot raster-scanner to scan at specified angles rather than only along specified axes. Alternatively, curve-follower normalization can be achieved by transforming the coordinate waveforms in a linear combining network. Second-order moments of the pattern are convenient properties to use in specifying the transformation. By mapping the original pattern into one having a scalar moment matrix all linear pattern variations can be removed. Comparison experiments with three sets of handprinted numerals showed that error rates were reduced by integral factors if the patterns were normalized before scanning for recognition.