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Automatic Indic script identification from handwritten documents: page, block, line and word-level approach

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Abstract

Script identification is a well-studied problem in literature since last decade. Several methods for automatic script identification have been reported. All these methods consider a document as either at page, block, line or word-level, but no experimental/empirical conclusion has been provided in choosing the particular level of work. To address this, we have carried out a multi-level script identification experiment, i.e., the same document is considered at different levels namely: page, block, line and word for script identification. Two different types of features are considered: script dependent and script independent, which is computed at each level to categorize different scripts. The experiment is conducted on a newly created handwritten multi-script and multi-level dataset, where 5 blocks, 7.5 lines and 15 words are generated from a single page, on an average (440 pages, 2200 blocks, 3300 lines and 6600 words, in total). Finally, we conclude two major issues: (1) find an optimal level of work, i.e. page/block/line/word-level, (2) provide a qualitative measure of feature set on particular level of work considered.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Aliah University, Kolkata, India

    Sk Md Obaidullah

  2. Department of Computer Science, University of South Dakota, Vermillion, SD, 57069, USA

    K. C. Santosh

  3. Department of Computer Science, West Bengal State University, Kolkata, India

    Chayan Halder & Kaushik Roy

  4. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Nibaran Das

Authors
  1. Sk Md Obaidullah
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  2. K. C. Santosh
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  3. Chayan Halder
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  4. Nibaran Das
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  5. Kaushik Roy
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Correspondence to K. C. Santosh.

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Obaidullah, S.M., Santosh, K.C., Halder, C. et al. Automatic Indic script identification from handwritten documents: page, block, line and word-level approach. Int. J. Mach. Learn. & Cyber. 10, 87–106 (2019). https://doi.org/10.1007/s13042-017-0702-8

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  • DOI: https://doi.org/10.1007/s13042-017-0702-8

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