Persian Handwriting Analysis Using Functional Principal Components

Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument, the handwriting would be an infinite dimensional data; a functional object. In this paper we try to use the functional principal components analysis (FPCA) to the Persian handwriting data, analyzing the word Mehr which is the Persian term for Love.