Word-Level Thirteen Official Indic Languages Database for Script Identification in Multi-script Documents

  • Sk Md ObaidullahEmail author
  • K. C. Santosh
  • Chayan Halder
  • Nibaran Das
  • Kaushik Roy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 709)


Without a publicly available database, we cannot advance research nor can we make a fair comparison with the state-of-the-art methods. To bridge this gap, we present a database of eleven Indic scripts from thirteen official languages for the purpose of script identification in multi-script document images. Our database is composed of 39K words that are equally distributed (i.e., 3K words per language). At the same time, we also study three different pertinent features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations, by using three different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA) and random forest (RF). In our test, using all features, MLP is found to be the best performer showing the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).


Multi-script documents Official indic script database Script identification 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Sk Md Obaidullah
    • 1
    Email author
  • K. C. Santosh
    • 2
  • Chayan Halder
    • 3
  • Nibaran Das
    • 4
  • Kaushik Roy
    • 3
  1. 1.Department of Computer Science and EngineeringAliah University KolkataWest BengalIndia
  2. 2.Department of Computer ScienceThe University of South DakotaVermillionUSA
  3. 3.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  4. 4.Department of Computer ScienceWest Bengal State UniversityKolkataIndia

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