Writer Identification from Handwritten Devanagari Script

  • Chayan Halder
  • Kishore Thakur
  • Santanu Phadikar
  • Kaushik Roy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)


This paper presents analysis of Devanagari characters for writer identification. Being originated from Brahmic script, Devanagari is the most popular script in India. It is used by over 400 million people around the world. Application of writer identification of Devanagari handwritten characters covers a vast area such as The Questioned Document Examination (QDE) is an area of the Forensic Science with the main purpose to answer questions related to questioned document (authenticity, authorship and others). Signature verification in banking, in Graphology (study of handwriting) a theory or practice for inferring a person’s character, disposition, and attitudes from their handwriting. Here we collect 5 copies of handwritten characters to nullify intra-writing variation, from 50 different people mainly students. After preprocessing and character extraction, 64-dimensional feature is computed based on gradient of the images. Some manual processing is required because some noises are too difficult to remove automatically as they are much closer to the characters. We have used LIBLINEAR and LIBSVM classifiers of WEKA environment to get the individuality of characters. We have done the writer identification with all the characters and obtained 99.12 % accuracy for LIBLINEAR with all writers. Features collected from this work can be used in the next level to identify writers from their cursive writing.


Individuality of handwriting Writer identification Devanagari handwriting analysis WEKA LIBLINEAR LIBSVM 



One of the author would like to thank Department of Science and Technology (DST) for support in the form of INSPIRE fellowship.


  1. 1.
    Hiremath, P.S., Shivashankar, S., Pujari, J.D., Kartik, R.K.: Writer identification in a handwritten document image using texture features. In: International Conference on Signal and Image Processing, pp. 139–142 (2010)Google Scholar
  2. 2.
    Srihari, S.N., Cha, S.H., Arora, H., Lee, S.: Individuality of handwriting, pp. 1–17. Report of National Criminal Justice Reference Services (2001)Google Scholar
  3. 3.
    Ramteke, A.S., Rane, M. E.: Offline handwritten devanagari script segmentation. Int. J. Sci. Technol. Res. 1(4), 142–145 (2012)Google Scholar
  4. 4.
    Patil, P.M., Ansari, S.: A research survey of devanagari handwritten word recognition. Int. J. Eng. Res. Technol. 2(10), 1010–1015 (2013)Google Scholar
  5. 5.
    Halder, C., Paul, J., Roy, K.: Individuality of Bangla numerals. In: Proceedings of 12th International Conference on Intelligent Systems Design and Applications, pp. 264–268 (2012)Google Scholar
  6. 6.
    Roy, K., Pal, U.: On the development of an OCR system for Indian postal automation. LAP LAMBERT Academic Publishing, Germany. ISBN: 978-38-443-1403-8 (2011)Google Scholar
  7. 7.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  8. 8.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)MATHGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Chayan Halder
    • 1
  • Kishore Thakur
    • 2
  • Santanu Phadikar
    • 2
  • Kaushik Roy
    • 1
  1. 1.Department of Computer ScienceWest Bengal State UniversityBarasat, KolkataIndia
  2. 2.Department of Computer ScienceWest Bengal University of TechnologySalt Lake, KolkataIndia

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