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

Sk Md Obaidullah, Chayan Halder, Nibaran Das and Kaushik Roy
Additional contact information
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

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2016, vol. 7, issue 1, 43-60

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.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 8/IJSSMET.2016010103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jssmet:v:7:y:2016:i:1:p:43-60

Access Statistics for this article

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) is currently edited by Ahmad Taher Azar

More articles in International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) from IGI Global
Bibliographic data for series maintained by Journal Editor ().