Handwriting synthesis and classification by means of space-variant transform and Karhunen–Loève analysis

Optical-pattern-recognition techniques are generally unable to provide an efficient approach to the classification of optical data, because of the linearity of the Fourier transform. A space-variant imaging model is proposed, whose behavior is characterized on the basis of orthonormal polynomials. The optical data are described in an orthonormal space based upon these polynomials. Proper-axis rotation and dimensionality reduction are supplied by the Karhunen–Loève transform. The intrinsic variables then become available, allowing the clustering of the data (considered as belonging to statistical classes). This work is illustrated by examples of handwriting recognition and classification.