Hexa-Directional Feature Extraction for Target-Specific Handwritten Digit Recognition

  • Sanjay Kumar SonbhadraEmail author
  • Sonali Agarwal
  • P. Nagabhushan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1085)


Handwritten numeral recognition (HNR) is the most challenging task in the area of optical character recognition (OCR). OCR process involves both feature extraction and selection that is being generated by central or distributed sources. Presence of unimportant features in a feature set may lead to the “curse of dimensionality” and causes malfunctioning of the recognition system. A feature set is claimed as a good feature set if it contains only useful and discriminative features. In this research, the proposed model considers the projection distance of available black pixels from sharp boundary edges from all four directions (left, right, top and bottom) and directional longest run length of black pixels for rows, columns, principal diagonal and off-diagonal of any handwritten digit/character. This feature extraction algorithm is advantageous because it yields less number of features compared to zone and projection distance-based approach thus reduces computation cost without compromising the classification accuracy. Two levels of experiments have been performed to validate the authenticity of the proposed approach. In the first level, we consider the group of confusing classes, e.g. (0, 6, 8 and 9) and perform one-class classification for target-specific mining using support vector data description (SVDD); whereas, in the second level we consider all classes from 0 to 9 and perform one-class classification. Experiments are performed on own generated and MNIST data sets. For both data sets, the proposed model demonstrates better results as compared to zoning and directional-based approach of feature extraction. This paper considers classification accuracy, training time and feature set size as comparison parameters.


Handwritten numeral recognition (HNR) Target specific Feature extraction Feature selection Support vector data description (SVDD) Digit recognition 


  1. 1.
    Chang, T., Chen, S.: Character segmentation using convex-hull techniques. Int. J. Pattern Recognit. Artif. Intell. 13(6), 833–858 (1999)CrossRefGoogle Scholar
  2. 2.
    Cheriet, M.: Extraction of handwritten data from noisy gray-level images using a multiscale approach. Pattern Recogn. Artif. Intell. 13(5), 665–684 (1999)CrossRefGoogle Scholar
  3. 3.
    Okamoto, M.: Online handwriting character recognition method using directional and direction-change features. Int. J. Pattern Recogn. Artif. Intell. 13(7), 1041–1059 (1999)CrossRefGoogle Scholar
  4. 4.
    Qian, K.: Gray image skeletonization with hollow preprocessing using distance transformation. Pattern Recogn. Artif. Intell. 13(6), 881–892 (1999)CrossRefGoogle Scholar
  5. 5.
    Su, T.: Chinese Handwriting Recognition: An Algorithmic Perspective, Springer (2013)Google Scholar
  6. 6.
    Alaei, A., Nagabhushan, P., Pal, U.: A new dataset of Persian handwritten documents and its segmentation. In: Proceedings 2011 7th Iranian Conference on Machine Vision Image Processing. MVIP, 2011Google Scholar
  7. 7.
    Alaei, U., Pal, U., Nagabhushan, P.: Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals. In: Proceedings 7th International Conference on Advances in Pattern Recognition ICAPR, 391–394 2009Google Scholar
  8. 8.
    Trier, D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition-a survey. Pattern Recogn. 29(4), 641–662 (1996)CrossRefGoogle Scholar
  9. 9.
    Alaei, A., Nagabhushan, P., Pal, U.: Fine classification of unconstrained handwritten Persian/Arabic numerals by removing confusion amongst similar classes. In: Proceedings International Conference Document Analysis and Recognition ICDAR, 601–605 2009Google Scholar
  10. 10.
    Bhattacharya, U., Chaudhuri, B.B.: Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE Trans. on PAMI 31(3), 444–457 (2009)CrossRefGoogle Scholar
  11. 11.
    Pal, U., Wakabayashi, T., Sharma, N., Kimura, F.: Handwritten numeral recognition of six popular indian scripts. In: Proceedings of 9th ICDAR, 749–753 2007Google Scholar
  12. 12.
    Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: Off-line handwritten character recognition of Devnagari script. In: Proceedings of 9th ICDAR, 496–500 2007Google Scholar
  13. 13.
    Kulkarni, S.R., Rajendran, B.: Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization. Neural Netw. 103, 118–127 (2018). ISSN 0893-6080Google Scholar
  14. 14.
    Cilia, N.D., De Stefano, D., Fontanella, F., di Freca, A.S.: A ranking-based feature selection approach for handwritten character recognition. Pattern Recognit. Lett. (2018)Google Scholar
  15. 15.
    Alaei, A., Pal, U., Nagabhushan, P.: A new scheme for unconstrained handwritten text-line segmentation. Pattern Recognit. 44(4), 917–928 (2011)CrossRefGoogle Scholar
  16. 16.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, p. 976. Nueva Jersey (2008)Google Scholar
  17. 17.
    Rajashekararadhya, S.V., Vanaja Ranjan, P., Manjunath, V.N.: Isolated handwritten Kannada and Tamil numeral recognition: zoning and a novel approach. In: First International Conference on Emerging Trends in Engineering and Technology ICETET 08, 1192–1195 2008Google Scholar
  18. 18.
    Das, N., et al.: A statistical-topological feature combination for recognition of handwritten numerals. Appl. Soft Comput. J. 12(8), 2486–2495 (2012)CrossRefGoogle Scholar
  19. 19.
    Dasgupta, J., Bhattacharya, K., Chanda, B.: A holistic approach for off-line handwritten cursive word recognition using directional feature based on Arnold transform. Pattern Recognit. Lett. 79, 73–79 (2016)CrossRefGoogle Scholar
  20. 20.
    Surinta, O., Karaaba, M.F., Schomaker, L.R., Wiering, M.A.: Recognition of handwritten characters using local gradient feature descriptors. Eng. Appl. Artif. Intell. 45 (2015)Google Scholar
  21. 21.
    Rajashekararadhya, S.V., Vanaja Ranjan, P.: Zone based feature extraction algorithm for handwritten numeral recognition of Kannada script. In: IEEE International Advance Computing Conference (IACC 2009), Patiala, India, 6–7 March 2009Google Scholar
  22. 22.
    Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Automatic recognition of handwritten numerical strings: a recognition and verification strategy. IEEE Trans. Pattern Recogn. Mach. Intell. 24(11), 1438–1454 (2002)CrossRefGoogle Scholar
  23. 23.
    Bao-Chang, P., Si-Chang, W., Guang-Yi, Y.: A method of recognizing handprinted characters. Plamondon, R.C., Suen, Y., Simner, M.L. (eds.) Computer Recognition and Human Production of Handwriting, World Scientific, pp. 37–60 (1989)Google Scholar
  24. 24.
    Manjunath Aradhya, V.N., Hemantha Kumar, G., Noushath, S.: Multilingual OCR system for South Indian scripts and English documents: an approach based on Fourier transform and principal component analysis. Eng. Appl. Artif. Intell. 21(4), 658–668, ISSN 0952-1976 2008Google Scholar
  25. 25.
    Boukharouba, A., Bennia, A.: Novel feature extraction technique for the recognition of handwritten digits. Appl. Comput. Informatics 13(1), 19–26 (2017)CrossRefGoogle Scholar
  26. 26.
    Mukhopadhyay, P., Chaudhuri, B.B.: A survey of Hough transform. Pattern Recognit. 48(3), 993–1010 (2015)CrossRefGoogle Scholar
  27. 27.
    Shivakumara, P., Hemantha Kumar, G., Guru, D.S., Nagabhushan, P.: A new boundary growing and Hough transform based approach for accurate skew detection in binary document images. In: Proceedings of International Conference on Intelligent Sensing and Information Processing (ICISIP), IEEE, 140–146 2005Google Scholar
  28. 28.
    Bull, D.R.: Transforms for image and video coding, communicating pictures, pp. 133–169. Academic Press (2014). ISBN 9780124059061Google Scholar
  29. 29.
    Shustorovich, A.: A subspace projection approach to feature extraction: the two-dimensional gabor transform for character recognition. Neural Netw. 7(8), 1295–1301 (1994). ISSN 0893-6080Google Scholar
  30. 30.
    Hadjadji, B., Chibani, Y., Nemmour, H.: An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis. Neurocomputing 265, 66–77 (2017)CrossRefGoogle Scholar
  31. 31.
    Arica, N., Yarman-Vural, F.T.: An Overview of character recognition focused on off-line handwriting. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 31(2), 216–233 (2001)Google Scholar
  32. 32.
    Chaudhuri, A., Mandaviya, K., Badelia, P., Ghosh, S.K.: Optical Character Recognition Systems for Different Languages with Soft Computing, vol. 352 (2017)Google Scholar
  33. 33.
    LeCun, Y., Bottou, L., Bengio, Y., Haffiner, P.: Gradient based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  34. 34.
    Zhu, W., Zhong, P.: A new one-class SVM based on hidden information. Knowl. Based Syst. 60, 35–43 (2014)CrossRefGoogle Scholar
  35. 35.
    Zhang, J., et al.: Watch, attend and parse: an end-to-end neural network based approach to handwritten mathematical expression recognition. Pattern Recogn. 71, 196–206 (2017)CrossRefGoogle Scholar
  36. 36.
    LeCun, Y., et al.: Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Networks Stat. Mech. Perspect. 2, 261–276 (1995)Google Scholar
  37. 37.
    Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recognit. Lett. 20(11–13), 1191–1199 (1999)CrossRefGoogle Scholar
  38. 38.
    Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54, 45–66 (2004)CrossRefGoogle Scholar
  39. 39.
    Ye, Q.L., Zhao, C.X., Zhang, H.F., Chen, X.B.: Recursive ‘concave-convex’ fisher linear discriminant with applications to face, handwritten digit and terrain recognition. Pattern Recognit. 45(1), 54–65 (2012)CrossRefGoogle Scholar
  40. 40.
    Jolliffe, T.: Principal Component Analysis. Springer-Verlag, New York (1986)CrossRefGoogle Scholar
  41. 41.
    Surinta, O., Karaaba, M.F., Schomaker, L.R.B., Wiering, M.A.: Recognition of handwritten characters using local gradient feature descriptors. Eng. Appl. Artif. Intell. 45, 405–414 (2015)CrossRefGoogle Scholar
  42. 42.
    Minter, T.: Single-class classification (1975)Google Scholar
  43. 43.
    Koch, M.W., Moya, M.M., Hostetler, L.D., Fogler, R.J.: Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition. Neural Networks 8(7–8), 1081–1102 (1995)CrossRefGoogle Scholar
  44. 44.
    Tax, D.M.J., Duin, R.P.W.: Uniform object generation for optimizing one-class classifiers. J. Mach. Learn. Res. 2, 155–173 (2001)zbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Indian Institute of Information TechnologyAllahabadIndia

Personalised recommendations