Advertisement

Recognition of Handwritten Meitei Mayek and English Alphabets Using Combination of Spatial Features

  • Sanasam Chanu Inunganbi
  • Prakash ChoudharyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Handwritten character recognition is an exciting and challenging topic in the field of pattern recognition because of massive variation in writing style and similar looking characters. Combining two different scripts boost the challenge to another level as each language has a unique peculiarity. The choice of distinguishing feature enhances the accuracy and efficiency of a recognition system. In this paper, we present spatial features based recognition of handwritten Manipuri (Meitei Mayek) and English alphabets. Background directional distribution, projection histogram, and uniform local binary pattern features have been used for extracting distinct feature for recognition by KNN classifier. The highest accuracy achieved in the proposed methodology is 92.40%.

Keywords

Handwritten character recognition Meitei Mayek English alphabets BDD PH ULBP 

References

  1. 1.
    Inunganbi, S., Choudhary, P.: Recognition of handwritten Meitei Mayek script based on texture feature. Int. J. Nat. Lang. Comput. (IJNLC) 7(5), 99–108 (2018)Google Scholar
  2. 2.
    de Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), pp. 273–280 (2009)Google Scholar
  3. 3.
    Badrinath, G., Gupta, P.: Stockwell transform based palm-print recognition. Appl. Soft Comput. 11(7), 4267–4281 (2011)CrossRefGoogle Scholar
  4. 4.
    Bashir, R., Quadri, S.: Identification of Kashmiri script in a bilingual document image. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP), pp. 575–579. IEEE (2013)Google Scholar
  5. 5.
    Bhattacharya, U., Chaudhuri, B.B.: Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 444–457 (2009)CrossRefGoogle Scholar
  6. 6.
    Chaudhari, S.A., Gulati, R.M.: An OCR for separation and identification of mixed English—Gujarati digits using KNN classifier. In: 2013 International Conference on Intelligent Systems and Signal Processing (ISSP), pp. 190–193. IEEE (2013)Google Scholar
  7. 7.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  8. 8.
    Dash, K.S., Puhan, N.B., Panda, G.: Handwritten numeral recognition using non-redundant stockwell transform and bio-inspired optimal zoning. IET Image Process. 9(10), 874–882 (2015)CrossRefGoogle Scholar
  9. 9.
    Drabycz, S., Stockwell, R.G., Mitchell, J.R.: Image texture characterization using the discrete orthonormal S-transform. J. Digit. Imaging 22(6), 696 (2009)CrossRefGoogle Scholar
  10. 10.
    Ghosh, S., Barman, U., Bora, P., Singh, T.H., Chaudhuri, B.: An OCR system for the Meetei Mayek script. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2013)Google Scholar
  11. 11.
    Hassan, T., Khan, H.A.: Handwritten bangla numeral recognition using local binary pattern. In: 2015 International Conference on, Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–4. IEEE (2015)Google Scholar
  12. 12.
    Ilmi, N., Budi, W.T.A., Nur, R.K.: Handwriting digit recognition using local binary pattern variance and K-Nearest Neighbor classification. In: 2016 4th International Conference on, Information and Communication Technology (ICoICT), pp. 1–5. IEEE (2016)Google Scholar
  13. 13.
    Jawahar, C., Kumar, M.P., Kiran, S.R.: A bilingual OCR for Hindi-Telugu documents and its applications. In: 2003 Proceedings of Seventh International Conference on Document Analysis and Recognition, pp. 408–412. IEEE (2003)Google Scholar
  14. 14.
    Khoddami, M., Behrad, A.: Farsi and latin script identification using curvature scale space features. In: 2010 10th Symposium on Neural Network Applications in Electrical Engineering (NEUREL), pp. 213–217. IEEE (2010)Google Scholar
  15. 15.
    Kumar, C.J., Kalita, S.K.: Recognition of handwritten numerals of manipuri script. Int. J. Comput. Appl. 84(17) (2013)Google Scholar
  16. 16.
    Laishram, R., Singh, A.U., Singh, N.C., Singh, A.S., James, H.: Simulation and modeling of handwritten Meitei Mayek digits using neural network approach. In: Proceedings of the International Conference on Advances in Electronics, Electrical and Computer Science Engineering-EEC, pp. 355–358 (2012)Google Scholar
  17. 17.
    Laishram, R., Singh, P.B., Singh, T.S.D., Anilkumar, S., Singh, A.U.: A neural network based handwritten Meitei Mayek alphabet optical character recognition system. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5. IEEE (2014)Google Scholar
  18. 18.
    Liu, C.L., Koga, M., Fujisawa, H.: Gabor feature extraction for character recognition: comparison with gradient feature. In: 2005 Proceedings of Eighth International Conference on Document Analysis and Recognition, pp. 121–125. IEEE (2005)Google Scholar
  19. 19.
    Mahmoud, S.A.: Arabic character recognition using Fourier descriptors and character contour encoding. Pattern Recogn. 27(6), 815–824 (1994)CrossRefGoogle Scholar
  20. 20.
    Mansinha, L., Stockwell, R., Lowe, R.: Pattern analysis with two-dimensional spectral localisation: applications of two-dimensional S transforms. Phys. A: Stat. Mech. Appl. 239(1–3), 286–295 (1997)CrossRefGoogle Scholar
  21. 21.
    Maring, K.A., Dhir, R.: Recognition of cheising iyek/eeyek-manipuri digits using support vector machines. Ijcsit 1(2) (2014)Google Scholar
  22. 22.
    Mowlaei, A., Faez, K., Haghighat, A.T.: Feature extraction with wavelet transform for recognition of isolated handwritten Farsi/Arabic characters and numerals. In: 14th International Conference on Digital Signal Processing, DSP 2002, vol. 2, pp. 923–926. IEEE (2002)Google Scholar
  23. 23.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  24. 24.
    Pati, P.B., Raju, S.S., Pati, N., Ramakrishnan, A.: Gabor filters for document analysis in Indian bilingual documents. In: 2004 of Proceedings of International Conference on Intelligent Sensing and Information Processing, pp. 123–126. IEEE (2004)Google Scholar
  25. 25.
    Philip, B., Samuel, R.S.: A novel bilingual OCR for printed Malayalam-English text based on gabor features and dominant singular values. In: 2009 International Conference on Digital Image Processing, pp. 361–365. IEEE (2009)Google Scholar
  26. 26.
    Rahiman, M.A., Adheena, C., Anitha, R., Deepa, N., Kumar, G.M., Rajasree, M.: Bilingual OCR system for printed documents in Malayalam and English. In: 2011 3rd International Conference on Electronics Computer Technology (ICECT), vol. 3, pp. 40–45. IEEE (2011)Google Scholar
  27. 27.
    Ray, A., Rajeswar, S., Chaudhury, S.: OCR for bilingual documents using language modeling. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1256–1260. IEEE (2015)Google Scholar
  28. 28.
    Shridhar, M., Badreldin, A.: High accuracy character recognition algorithm using Fourier and topological descriptors. Pattern Recogn. 17(5), 515–524 (1984)CrossRefGoogle Scholar
  29. 29.
    Stockwell, R.G., Mansinha, L., Lowe, R.: Localization of the complex spectrum: the S transform. IEEE Trans. Sig. Process. 44(4), 998–1001 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology ManipurImphalIndia

Personalised recommendations