Handwritten Bangla City Name Recognition Using Shape-Context Feature

  • Samanway Sahoo
  • Subham Kumar Nandi
  • Sourav Barua
  • Pallavi
  • Samir Malakar
  • Ram Sarkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


A segmentation-free approach is proposed to recognize the handwritten city names written in Bangla script. Initially, all the word images are converted into virtually single connected component following the refraction properties of light in order to design a unique shape-context of the same. Then a 64-dimensional feature vector is estimated from the said shape-context of each word image. A database containing 150 samples of 50 most popular city names of West Bengal, a state of India is prepared for evaluating the present method. Performance of this feature vector is also compared with some recently published feature vectors, and it is observed that the newly designed feature vector has outperformed the others.


Shape-context feature Handwritten word recognition City name recognition Bangla script 


  1. 1.
    Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., Basu, D.K.: A hierarchical approach to recognition of handwritten Bangla characters. Pattern Recogn. 42(7), 1467–1484 (2009)CrossRefGoogle Scholar
  2. 2.
    Bhowmik, S., Polley, S., Roushan, M.G., Malakar, S., Sarkar, R., Nasipuri, M.: A holistic word recognition technique for handwritten Bangla words. Int. J. Appl. Pattern Recogn. 2(2), 142–159 (2015)CrossRefGoogle Scholar
  3. 3.
    Sarkar, R., Malakar, S., Das, N., Basu, S., Kundu, M., Nasipuri, M.: Word extraction and character segmentation from text lines of unconstrained handwritten Bangla document images. J. Intell. Syst. 20(3), 227–260 (2011)Google Scholar
  4. 4.
    Bhowmik, S., Roushan, M.G., Polley, S., Malakar, S., Sarkar, R., Nasipuri, M.: Handwritten Bangla word recognition using HOG descriptor. In: Fourth International Conference of Emerging Applications of Information Technology (EAIT), pp. 193–197. IEEE (2014)Google Scholar
  5. 5.
    Bhowmik, S., Malakar, S., Sarkar, R., Nasipuri, M.: Handwritten Bangla word recognition using elliptical features. In: 2014 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 257–261. IEEE (2014)Google Scholar
  6. 6.
    Vajda, S., Roy, K., Pal, U., Chaudhuri, B.B., Belaid, A.: Automation of Indian postal documents written in Bangla and English. Int. J. Pattern Recogn. Artif. Intell. 23(08), 1599–1632 (2009)CrossRefGoogle Scholar
  7. 7.
    Malakar, S., Sharma, P., Singh, P.K., Das, M., Sarkar, R., Nasipuri, M.: A holistic approach for handwritten Hindi word recognition. Int. J. Comput. Vis. Image Process. 7(1), 59–78 (2017)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Tamen, Z., Drias, H., Boughaci, D.: An efficient multiple classifier system for Arabic handwritten words recognition. Pattern Recogn. Lett. 93, 123–132 (2017)CrossRefGoogle Scholar
  10. 10.
    Lavrenko, V., Rath, T.M., Manmatha, R.: Holistic word recognition for handwritten historical documents. In: Proceedings of First International Workshop on Document Image Analysis for Libraries, pp. 278–287. IEEE (2004)Google Scholar
  11. 11.
    Acharyya, A., Rakshit, S., Sarkar, R., Basu, S., Nasipuri, M.: Handwritten word recognition using MLP based classifier: a holistic approach. Int. J. Comput. Sci. Issues 10(2), 422–427 (2013)Google Scholar
  12. 12.
    Sarkar, R., Das, N., Basu, S., Kundu, M., Nasipuri, M., Basu, D.K.: CMATERdb1: a database of unconstrained handwritten Bangla and Bangla-English mixed script document image. Int. J. Doc. Anal. Recogn. (IJDAR) 15(1), 71–83 (2012)CrossRefGoogle Scholar
  13. 13.
    Languages with at least 50 million first-language speakers. Accessed from Summary by language size Ethnologue
  14. 14.
    Das, B., Bhowmik, S., Saha, A., Sarkar, R.: An adaptive foreground-background separation method for effective binarization of document images. In: proceedings on 8th International Conference on Soft Computing and Pattern Recognition (2016)Google Scholar
  15. 15.
  16. 16.
    Freeman, H.: On the encoding of arbitrary geometric configurations. IRE Trans. Electron. Comput. 10, 260–268 (1961)MathSciNetCrossRefGoogle Scholar
  17. 17.
    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

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Samanway Sahoo
    • 1
  • Subham Kumar Nandi
    • 1
  • Sourav Barua
    • 1
  • Pallavi
    • 1
  • Samir Malakar
    • 2
  • Ram Sarkar
    • 3
  1. 1.Department of Computer Science and TechnologyIndian Institute of Engineering Science and Technology, ShibpurShibpurIndia
  2. 2.Department of Computer ScienceAsutosh CollegeKolkataIndia
  3. 3.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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