A principal component approach to classification of handwritten words

Presents an efficient technique for the classification of off-line handwritten words into upper and lower case using principal components (PC). The technique consists of two phases. For each word, in the feature extraction phase, first the boundary points of the word are extracted, then 26 features, including global, local, regional and dominant features, are extracted using the contour information. In the classification phase, a discriminant function based on the PC, adapted by our system, is introduced to integrate the extracted features and classify words into upper and lower case. Experimental results show that the system achieves 83% correct word case classification for about 2240 test words randomly selected from a data set of 3226 words obtained from 12 writers