An Efficient Feature Extraction Method for Handwritten Character Recognition

  • Manju Rani
  • Yogesh Kumar Meena
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)


Handwritten character recognition in a particular language is one of the favourite topics for research from two last decades. Image processing and pattern recognition plays a lead role in handwritten character recognition. It is not a easy task to build a program to achieve hundred percent accuracy for handwritten characters because even humans too make mistakes to recognize characters. There are three main steps of handwritten character recognition- Data collection and preprocessing, feature extraction and classification. Data collection includes creating a raw file of handwritten character images. Preprocessing steps are applied to find a normalized binary image of handwritten character which is easy to process in next step. Feature extraction is the process of gathering data of different samples so that on the basis of this data we can classify samples with different features. Feature extraction from preprocessed handwritten character plays the most important role in character recognition. Thus feature extraction stage in handwritten character recognition system has a large scope for researchers. In this paper, we also introduce a new feature extraction method for handwritten characters named Cross-corner. We use the results of some promising feature extraction methods to find the best method for this application.


Preprocessing Feature Extraction Recognition rate Classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Heutte, L., Paquet, T., Moreau, J.V., Lecourtier, Y., Olivier, C.: A structral/statistical feature based vector for handwritten character recognition. Pattern Recognition Letters 19, 629–641 (1998)CrossRefGoogle Scholar
  2. 2.
    Blumenstien, M., Verma, B., Basli, H.: A novel feature extraction technique for the recognition of segmented handwritten characters. In: Proceeding of Seventh Internatonal Conference on Document Analysis and Recognition (2003)Google Scholar
  3. 3.
    Rajashekararadhya, S.V., Vanaja Ranjan, P.: Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south indian scripts. Journal of Theoretical and Applied Information Technology, 1171–1180 (2005-2008)Google Scholar
  4. 4.
    Ganapathy, V., Liew, K.L.: Handwritten character recognition using mutiscale neural network training technique. World Acedamy of Science, Engineering and Technology, 32–37 (2008)Google Scholar
  5. 5.
    Arora, S., Bhattacharjee, D., Nasipuri, M., Basu, D.K., Kundu, M.: Combining the multiple feature extraction techniques for handwritten devnagari character recognition. In: IEEE Region 10 Colloqurium and the Third ICIIS, Kharagpur, India, pages 110 (2008)Google Scholar
  6. 6.
    Pal, A., Singh, D.: Handwritten English Character Recognition using Neural Network. International Journal of Computer Science & Communications, 141–144 (2010)Google Scholar
  7. 7.
    Zhong, C., Ding, Y., Fu, J.: Handwritten character recognition based on 13-point feature of skeleton and self-organizing competition network. In: International Conference on Intelligent Computation Technology and Automation, pp. 414–417 (2010)Google Scholar
  8. 8.
    Pradeep, J., Srinivasan, E., Himavathi, S.: Diagonal feature extraction based on handwritten character using neural network. International Journal of Computer Applications 8, 17–21 (2010)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Huang, T.-L., yu Liu, X.: Handwritten character recognition based on BP neural network. In: Third International Conference on Genetic and Evolutionary Computing, pp. 520–524 (2009)Google Scholar
  10. 10.
    Singh, D., Singh, S.K., Dutta, M.: Handwritten Character Recognition Using Twelve Directional Feature Input and Neural Network. International Journal of Computer Applications 1, 82–85 (2010)CrossRefGoogle Scholar
  11. 11.
    Araki, N., Okuzaki, M., Konishi, Y., Ishigaki, H.: A Statistical Approach for Handwritten Character Recognition Using Bayesian Filter. In: 3rd International Conference on Innovative Computing Information and Control, pp. 194–198 (2008)Google Scholar
  12. 12.
    Smith, S.J., Bourgoin, M.O., Sims, K., Voorhees, H.L.: Handwritten character classification using nearest neighbor in large databases. IEEE Pattern Analysis and Machine Intelligence 16, 915–919 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manju Rani
    • 1
  • Yogesh Kumar Meena
    • 1
  1. 1.Malaviya National Institute of TechnologyJaipurIndia

Personalised recommendations