Word-Level Handwritten Script Identification from Multi-script Documents

  • Mallikarjun Hangarge
  • K. C. Santosh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 266)


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.


Discrete cosine transform Rotation invariant features Script identification Multi-script document 


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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Karnatak Arts, Science and Commerce CollegeBidarIndia
  2. 2.Communications Engineering BranchUS National Library of Medicine National Institutes of HealthBethesdaUSA

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