Improved handwritten character recognition using second-order information from training set
Z.M. Kovacs, Riccardo Rovatti, Renato A. Guerrieri, Roberto Ragazzoni · Electronics Letters · 1993
The problem of improving the capability of statistical character classifiers based on finite and sparse training sets is addressed. A significant improvement is obtained coupling standard classifiers based on the k-nearest neighbours technique with a second higher level classification stage. This method has been applied to three existing classifiers reducing the error rate at zero rejection of ̃ 17%.