Study of Different Features and Classification Techniques for Recognition of Handwritten Devanagari Text

  • Abstract
  • Keywords
  • References
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  • Abstract

    Devanagari script is most popular and an older script in India. Millions of people all over the globe are using Devanagri script for various purposes such as communication, understanding the history, record keeping, research, etc.  Recognition of handwritten Devanagari word is one of the popular area of research from decades because of its wide scope of applications. Different features and techniques of classification are the most important steps in the process of recognizing Devanagari handwritten word, are described in this paper.



  • Keywords

    classification; Devanagari script recognition; feature extraction; feature selection; pattern recognition.

  • References

      [1] Sk Md Obaidullah, Supratik Kundu Das , Kaushik Roy , "A System for Handwritten Script Identification from Indian Document", Researchgate: Journal of Pattern Recognition Research 8 (2013) 1-12, Researchgate, 2013, pp.1-12.

      [2] Mallikarjun Hangarge, Santosh K.C., Rajmohan Pardeshi, "Directional Discrete Cosine Transform for Handwritten Script Identification", 12th International Conference on Document Analysis and Recognition (ICDAR), 2013, IEEE, 2013, pp.344-348.

      [3] DEEPTI KHANDUJA, NEETA NAIN, and SUBHASH PANWAR, "A Hybrid Feature Extraction Algorithm for Devanagari Script", ACM Trans. Asian Low-Resour. Lang. Inf. Process., Vol. 15, No. 1, Article 2, ACM, 2015, pp.1-10.

      [4] K. Roy, S. Kundu Das, Sk Md Obaidullah, "Script Identification from Handwritten Document", Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 2011, Researchgate, 2011, pp.1-5.

      [5] Komal V. Rayate, Shyamrao V. Gumaste, “Image Classification-Review” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 5, Issue 10, October 2016

      [6] Hiremath P. S., Shivashankar S., Jagdeesh D. Pujari, V. Mouneswara, "Script identification in a handwritten document image using texture features ", IEEE Explore, IEEE, 2010, pp.110-114.

      [7] G. G. Rajput, Anita H. B, "Handwritten Script Recognition using DCT and Wavelet Features at Block Level", RTIPPR 2010, IJCA, 2010, pp.158-163.

      [8] Mallikarjun Hangarge, B.V.Dhandra, "Offline handwritten script identification in document images", IJCA 2010 Vol.4,No.6, IJCA, 2010, pp.6-10.

      [9] Sukalpa Chanda, Srikanta Pal, Katrin Franke, Umapada Pal, "Two-stage Approach for Word-wise Script Identification", 2009 10th ICDAR, IEEE, 2009, pp.926-930.

      [10] K. Roy, K. Majumder, "Trilingual Script Separation of Handwritten Postal Document", Sixth Indian Conference on Computer Vision, Graphics & Image Processing, 2008. ICVGIP '08. , IEEE, 2008, pp.693-700.

      [11] Ramesh M. Kagalkar, S.V Gumaste, “Gradient Based Key Frame Extraction for Continuous Indian Sign Language Gesture Recognition and Sentence Formation in Kannada Language: A Comparative Study of Classifiers”, International Journal of Computer Sciences and Engineering, Vol.-4(9), Sep 2016

      [12] Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu , "Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition", 2008 IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10., IEEE, 2008, pp.342-1-342-6.

      [13] B.V.Dhandra, Mallikarjun Hangarge, "Global and Local Features Based Handwritten Text Words and Numerals Script Identification", International Conference on Computational Intelligence and Multimedia Applications 2007, IEEE, 2007, pp.471-475.

      [14] Komal V. Rayate, Shyamrao V. Gumaste “Classification of High Resolution Images with Different Cues by using CRF Model”, Sandip Foundation's International Journal on Emerging Trends in Technology (IJETT), Volume 4, issue 1 April 2017

      [15] Gopal Datt Joshi, Saurabh Garg, Jayanthi Sivaswamy, "A generalised framework for script identification", IJDAR (2007), Springer, 2007, pp.55-68.

      [16] B.V.Dhandra, Mallikarjun Hangarge, "Global and Local Features Based Handwritten Text Words and Numerals Script Identification", International Conference on Computational Intelligence and Multimedia Applications 2007, IEEE, 2007, pp.471-475.

      [17] K.-Roy, A. Banerjee and U. Pal, "A System for Word-wise Handwritten Script Identification for Indian Postal Automation", IEEE INDIA ANNUAL CONFERENCE 2004, INDICON 2004, IEEE, 2004, pp.266-271.

      [18] Jinjie Huang, Yunze Cai, Xiaoming Xu, "A hybrid genetic algorithm for feature selection wrapper based on mutual information", Pattern Recognition Letters 28 (2007), Elsevier, 2007, pp.1825-1844.

      [19] Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin, "LIBLINEAR: A Library for Large Linear Classification", Journal of Machine Learning Research 9 (2008), JMLR, 2008, pp.1871-1874.

      [20] Kekre H B, Kharat M U, Sange S R, "Image data compression using new Halftoning operators and Run Length Encoding", Thinkquest~2010, Springer India, 2011, pp.208-213.





Article ID: 28285
DOI: 10.14419/ijet.v7i4.19.28285

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