Reader System for Transliterate Handwritten Bilingual Documents

  • Ranjana S. Zinjore
  • R. J. RamtekeEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


India is a Multistate- Multilingual country. Most of the people in India used their state official language and English is treated as a binding language used for form filling or some official work. So there is a need to create a system which will convert the handwritten bilingual document into digitized form. This paper aims at development of reader system for handwritten bilingual (Marathi-English) documents by recognizing words. This facilitates many applications such as Natural language processing, School, Society, Banking, post office and Library automation. The proposed system is divided into two phases. The first phase focuses on recognition of handwritten bilingual words using two different feature extraction methods including combination of structural and statistical method and Histogram of Oriented Gradient Method. K-Nearest Neighbor classifier is used for recognition. This classifier gives 82.85% recognition accuracy using Histogram of Oriented Gradient method. The dataset containing 4390 words collected from more than 100 writers. The second phase focuses on digitization and transliteration of recognized words and conversion of transliterated text into speech, which is useful in the society for visually impaired people.


Reader system Transliteration Handwritten bilingual document Histogram of oriented gradient 


  1. 1.
    Balakrishnan, K., et al.: Offline handwritten recognition of Malayalam district name-a holistic approach. arXiv preprint arXiv:1705.00794 (2017)
  2. 2.
    Belaïd, A., Santosh, K.C., d’Andecy, V.P.: Handwritten and printed text separation in real document. arXiv preprint arXiv:1303.4614 (2013)
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  4. 4.
    Dhandra, B.V., Hangarge, M.: Morphological reconstruction for word level script identification. Int. J. Comput. Sci. Secur. (IJCSS) 1(1), 41–51 (2007)Google Scholar
  5. 5.
    Gatos, B., Pratikakis, I., Kesidis, A.L., Perantonis, S.J.: Efficient off-line cursive handwriting word recognition. In: Tenth International Workshop on Frontiers in Handwriting Recognition, Suvisoft (2006)Google Scholar
  6. 6.
    Kamble, P.M., Hegadi, R.S.: Handwritten Marathi basic character recognition using statistical method (2014)Google Scholar
  7. 7.
    Sandyal, K.S., Patel, M.S.: Offline handwritten Kannada word recognition. pp. 19–22 (2014)Google Scholar
  8. 8.
    Manoj Kumar, P., Chandran, S.: Handwritten Malayalam word recognition system using neural networks. Int. J. Eng. Res. Technol. 4, 90–99 (2015)Google Scholar
  9. 9.
    Obaidullah, S.M., Santosh, K.C., Das, N., Halder, C., Roy, K.: Handwritten Indic script identification in multi-script document images: a survey. Int. J. Pattern Recogn. Artif. Intell. 32(10), 1856012 (2018)CrossRefGoogle Scholar
  10. 10.
    Obaidullah, S.M., Santosh, K.C., Halder, C., Das, N., Roy, K.: Automatic Indic script identification from handwritten documents: page, block, line and word-level approach. Int. J. Mach. Learn. Cybern. 10, 1–20 (2017)Google Scholar
  11. 11.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  12. 12.
    Pal, U., Chaudhuri, B.B.: Automatic separation of words in multi-lingual multi-script Indian documents. In: ICDAR, p. 576. IEEE (1997)Google Scholar
  13. 13.
    Patel, M.S., Kumar, R., Linga Reddy, S.C.: Offline Kannada handwritten word recognition using locality preserving projection (LPP) for feature extraction. IJIRSET, 4(7) (2015)Google Scholar
  14. 14.
    Patel, M.S., Reddy, S.L., Naik, A.J.: An efficient way of handwritten English word recognition. In: Satapathy, S.C., Biswal, B.N., Udgata, S.K., Mandal, J.K. (eds.) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. AISC, vol. 328, pp. 563–571. Springer, Cham (2015). Scholar
  15. 15.
    Ansari, S., Patil, P.M.: A research survey of Devnagari handwritten word recognition. Int. J. Eng. Res. Technol. 2(10), 1010–1015 (2013)Google Scholar
  16. 16.
    Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)CrossRefGoogle Scholar
  17. 17.
    Shaikh, M.A., Dagade, M.R.: Offline recognition of handwritten devanagari words using hidden markov model. IJIRST, 1(11) (2015)Google Scholar
  18. 18.
    Shaw, B., Parui, S.K., Shridhar, M.: Offline handwritten Devanagari word recognition: a segmentation based approach. In: 2008 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)Google Scholar
  19. 19.
    Singh, B., Mittal, A., Ansari, M.A., Ghosh, D.: Handwritten Devanagari word recognition: a curvelet transform based approach. Int. J. Comput. Sci. Eng. 3(4), 1658–1665 (2011)Google Scholar
  20. 20.
    Student, R.V.: Off-line handwritten Kannada text recognition using support vector machine using zernike moments. IJCSNS 11(7), 128 (2011)Google Scholar
  21. 21.
    Zinjore, R.S., Ramteke, R.J., Pathak, V.M.: Segmentation of merged lines and script identification in handwritten bilingual documents. In: Proceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation, pp. 29–32. ACM (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.KCES’s Institute of Management & ResearchJalgaonIndia
  2. 2.School of Computer SciencesNorth Maharashtra UniversityJalgaonIndia

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