Optical pattern recognition and clustering: Karhunen-Loève analysis.

A Fourier transform gives a first dimensionally reduced description of optical data. But it is not sensitive to statistical variations characterizing class properties and allowing clustering and statistical recognition. A Karhunen-Loève transform of Fourier spectra leads to a more classifying space: it is shown, through examples of writings, that clustering of optical data (especially recognition of scriptors) is achieved in a 2-D Karhunen-Loève space. Inner evolution of data belonging to a given class is described in a 3-D KL space, allowing the dating of texts.