Analysis on Efficient Handwritten Document Recognition Technique Using Feature Extraction and Back Propagation Neural Network Approaches

  • Pramit Brata ChandaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)


Today, Handwritten recognition becomes a very much thrust area in the field of pattern recognition and image processing. Handwritten recognition methods are used in real-life fields such as banking checks, car plates number identification, recognition of ZIP code, mail sorting, reading of different commercial forms, etc. The work is presented in this paper a system of handwriting of English document recognition based on feature extraction of the character. Almost 400 handwritten numerals are collected from different datasets as for sample for the classification purposes. The main work is presented here are consisting of several steps like preprocessing, feature extraction and Multi-Layer Perceptron model of neural network for classifying handwritten digits separately. Basically, Offline Recognition of English handwriting using multilayer perceptron network or back propagation networks are described throughout the entire work. First, the English alphabets are used as features introducing features sets of different English handwritten documents. Then the neural network is used tos train the datasets. Different types of training methods of back propagation network are used for calculating performance of the entire system. The recognition methods are based on back propagation network for analyzing the classification performance of handwritten documents. Here the system achieves the accuracy more than 90% using this efficient back propagation neural network based classification and feature extraction methods using morphological operations based zones separation scheme of digits. Here, the performance performance parameter like sensitivity, specificity, recall, accuracy provides more than 90% of rates indicates that better classification of handwritten documents.


Handwritten character recognition Recognition accuracy Back propagation Learning Resilient Recall MSE Feature extraction Sensitivity 


  1. 1.
    Ding, K., Liu, Z., Jin, L., Zhu, X.: A comparative study of GABOR feature and gradient feature for handwritten 17hinese character recognition. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 1182–1186. Beijing, China, 2–4 Nov 2007Google Scholar
  2. 2.
    Pranob, K., Charles, Harish, V., Swathi, M., Deepthi, CH.: A review on the various techniques used for optical character recognition. Int. J. Eng. Res. Appl. 2(1), 659–662, (2012)Google Scholar
  3. 3.
    Sharma, O P., Ghose, M. K., Shah, K.B.: An improved zone based hybrid feature extraction model for handwritten alphabets recognition using euler number. Int. J. Soft. Comput. Eng. 2(2), 504–58 (2012)Google Scholar
  4. 4.
    Pradeepa, J., Srinivasana, E., Himavathib, S.: Neural network based recognition system integrating feature extraction and classification for english handwritten. Int. J. Eng. 25(2), 99–106 (2012)CrossRefGoogle Scholar
  5. 5.
    Cheng-Lin, L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognit. 37(2), 265–279 (2004)Google Scholar
  6. 6.
    Deshmukh, S., Ragha, L.: Analysis of directional features—stroke and contour for handwritten character recognition. IEEE International Advance Computing Conference, India, pp. 1114–1118, 6–7 Mar 2009Google Scholar
  7. 7.
    Srihari, S.N.: Recognition of handwritten and machine printed text for postal address interpretation. Patterns Recogn. Lett. 14, 291–302 (1993)CrossRefGoogle Scholar
  8. 8.
    Dudarin, A., Kovacic, Z.: Alphanumerical Character Recognition Based on Morphological Analysis. IEEE (2010)Google Scholar
  9. 9.
    Suen, C.Y., Nadal, C. et al.: Computer recognition of unconstrained handwritten numerals. Proc. IEEE. 80, 1162–1180 (1992)Google Scholar
  10. 10.
    Neves, R.F.P. et al.: A new technique to threshold the courtesy amount of brazilian bank checks. In: Proceedings of 15th IWSSIP. IEEE Press, June 2008Google Scholar
  11. 11.
  12. 12.
    Patil, V., Shimpi, S.: Handwritten english character recognition using neural network. Elixir. Comput. Sci. Eng. 41, 5587–5591 (2011)Google Scholar
  13. 13.
    Chanda, P.B., Datta, S., Choudhury, J.P.: Analysis of character recognition using back propagation neural network algorithm. In: Proceedings of National Conference on Brain and Conciousness, Sept 2013Google Scholar
  14. 14.
    Chanda, P.B., Datta, S., Mukherjee, S., Goswami, S., Bisi, S.: Comparative analysis of offline character recognition using neural network approaches. In: ETCC 2014, LNEE. Springer 22–23 Mar 2014Google Scholar
  15. 15.
    Fujisawa, H.: Forty years of research in character and document recognition—an industrial perspective. Pattern Recogn. 41(8), 2435–2446 (2008)CrossRefGoogle Scholar
  16. 16.
    Sivanandam, S.N., Deepa, S.N.: Principles of Soft ComputingGoogle Scholar
  17. 17.
    Kowaliw, T., Kharma, N., Jensen, C., Mognieh, H., Yao, J.: Using competitive co-evolution to evolve better pattern recognizers. Int. J. Comput. Intell. Appl. 5(3), 305–320 (2005)CrossRefGoogle Scholar
  18. 18.
    Singh, D., Dutta M., Singh, SH.: Neural network based handwritten hindi character recognition. ACM Int. J. Mach. Learn. Comput. 2(4) (2012)Google Scholar
  19. 19.
    Amit, G., Kosta, Y. P., Gaurang, P., Chintan, G.: Initial classification through back propagation in a neural network following optimization through ga to evaluate the fitness of an algorithm. Int. J. Comput. Sci. Inf. Technol. (IJCSIT), 3(1) (2011)Google Scholar
  20. 20.
    Pal, A., Singh, D.: Handwritten English character recognition using neural network. Department of Computer Science & Engineering, U.P. Technical University, Lucknow, India Int. J. Comput. Sci. Commun. 1(2), pp. 141–144. (20100Google Scholar
  21. 21.
    Binti, N., Hamid, A.: The effect of adaptive parameters on the performance of back propagation. PhD Disseration, Faculty of Computer Science and Information Technology. University Tun Hussein Onn Malaysia, (2012)Google Scholar
  22. 22.
    Liu, C.-L., Jaeger, S., Nakagawa, M.: Online recognition of Chinese characters: the stateof-the-art. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 198–213 (2004)CrossRefGoogle Scholar
  23. 23.
    Dey, E.: Recognition Bangla and English Text from the Same Document, July 2009Google Scholar
  24. 24.
    Pal, U., Choudhuri, B.B.: Indian script character recognition:a survey. Pattern Recogn. 37, 1887–1899 (2004)CrossRefGoogle Scholar
  25. 25.
    Mori, S., Suen, C.Y., Kamamoto, K.: Historical review of OCR research and development. Proc. IEEE 80(July), 1029–1058 (1992)CrossRefGoogle Scholar
  26. 26.
    Singh, M.P., Dhaka, V.S.: Handwritten Character Recognition Using Modified Gradient Descent Technique of Neural Networks and Representation of Conjugate Descent for Training PatternsGoogle Scholar
  27. 27.
    Mathur, S., Aggarwal, V., Joshi, H., Ahlawat, A.: Offline handwriting recognition using genetic algorithm. 6th International Conference on Information Research and Applications, Varna, Bulgaria, June-July (2008)Google Scholar
  28. 28.
    Mamedov, F., Abu Hasna, J.F.: Character Recognition using Neural Network. Near East University, North Cyprus, Turkey via Mersin-10, KKTCGoogle Scholar
  29. 29.
    Devireddy, S.K., Rao, S.A.: Handwritten character recognition using back propagation network. JATIT (2005–2009)Google Scholar
  30. 30.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, USA (2002)Google Scholar
  31. 31.
    Singh, R., Yadav, C.S., Verma, P., Yadav, V.: Optical character recognition (OCR) for printed devnagari script using artificial neural network. Int. J. Comput. Sci. Commun. 1(1), 91–95 (2010)Google Scholar
  32. 32.
    Ganapathy, V., Liew, K.L.: Handwritten character recognition using multiscale neural network training technique. World Acad. Sci. Eng. Technol. 39 (2008)Google Scholar
  33. 33.
    Cheriet, M., Kharma, N., Liu, C.-L., Suen, C.Y.: Character Recognition Systems, a Guide for Students and Practitioners. Wiley, New Jersey (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKalyani Government Engineering CollegeKalyani, NadiaIndia

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