Performance evaluation of nonlinear shape normalization methods for the recognition of large-set handwritten characters

Recently, several nonlinear shape normalization methods have been proposed in order to compensate for shape distortions in large-set handwritten characters. The authors review these methods from the two points of view: feature projection and feature density equalization. The former makes a feature projection histogram by projecting a certain feature at each point into horizontal- or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. Then, a quantitative evaluation for these methods has been made based on the following criteria: recognition rate, processing speed, computational complexity, and degree of variation