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Bangla and Oriya Script Lines Identification from Handwritten Document Images in Tri-script Scenario

Author

Listed:
  • Sk Md Obaidullah

    (Aliah University, Kolkata, India)

  • Chayan Halder

    (West Bengal State University, Kolkata, India)

  • Nibaran Das

    (Jadavpur University, Kolkata, India)

  • Kaushik Roy

    (West Bengal State University, Kolkata, India)

Abstract

In this paper, two popular eastern Indian scripts namely Bangla and Oriya are considered for Line-level script identification considering two Tri-script groups where Devnagari and Roman are kept common in each group. A 27 dimensional feature vector has been constructed using FD (Fractal Dimension) and IMT (Interpolated Morphological Transform). 600 Line-level handwritten document images of each Tri-script groups have been considered for experimentation. Promising results has been found using multiple classifiers where MLP (Multi-Layer Perceptron) Neural Network and LMT (Logistic Model Tree) perform best for BDR (Bangla-Devnagari-Roman) combinations with 97% accuracy and LMT outperforms over others for ODR (Oriya-Devnagari-Roman) combinations with 97.7% accuracy. Bi-script performance analysis has also been made where combinations BR (Bangla-Roman) and BD (Bangla-Devnagari) results with accuracy of 98% and 97.5% respectively for the first group. Whereas for the second group OD (Oriya-Devnagari) and OR (Oriya-Roman) shows an accuracy of 98.25% and 98% respectively.

Suggested Citation

  • Sk Md Obaidullah & Chayan Halder & Nibaran Das & Kaushik Roy, 2016. "Bangla and Oriya Script Lines Identification from Handwritten Document Images in Tri-script Scenario," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 7(1), pages 43-60, January.
    • Handle: RePEc:igg:jssmet:v:7:y:2016:i:1:p:43-60
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