Abstract
In this paper, we present the directional discrete cosine transform (DCT) based rotation invariant features for word-level handwritten script identification. Our aim in this paper is two folds: one is to validate the effectiveness of the directional DCT (D-DCT) in extracting edge information of the studied word image and another is to provide rotation invariant property since conventional DCT (C-DCT) does not offer both issues. For each extracted word image, we compute DCT, its coefficient matrix and decompose into different directions such as horizontal, vertical, left and right diagonals plus mean and standard deviations of the decomposed components. These statistical features are then evaluated with hundreds of word images from six different scripts by using linear discriminant analysis (LDA) and achieved an accuracy of 97.35 % in average.
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