Style Context with Second-Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents an efficient technique for classification of off-line hand-written words into upper and lower case using principal components (PC). The technique consists of two phases. For each word, in feature extraction phase, first the boundary points of the word are extracted, then twenty-six features including global, local, region and dominants 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 3226 data set obtained from 12 writers