Advertisement

Zone Centroid Distance and Standard Deviation Based Feature Matrix for Odia Handwritten Character Recognition

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)

Abstract

Optical character recognition (OCR) is a type of document image analysis where scanned digital image that contains either machine printed or handwritten script input into an OCR software engine and translating it into an editable machine readable digital text format. In this paper we designed a novel and robust two stage recognition system for Odia handwritten characters as well as we prepare a standard deviation and zone centroid average distance based feature matrix for more accuracy while training and testing the Neural Network. The OHCR System is based on the algorithm of feed forward BPNN in two stage to perform the optimum feature extraction and recognition. The Odia characters are classified into four groups according to similarity of their shapes and features. The system uses ANN in two stages, having different parameters, the first stage classifies the characters into similar groups and in the second stage individual characters are recognized.

Keywords

Zone ANN centroid Character Recognition Morphological analysis Standard deviation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rejean, P., Srihari Sargur, N.: On-line and Off-line Handwriting Recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)CrossRefGoogle Scholar
  2. 2.
    Prema, K.V., Subba, R.N.V.: Two-tier architecture for unconstrained handwritten character recognition. Sadhna 27, Part 5, 585–594 (2002)Google Scholar
  3. 3.
    Tripathy, N., Pal, U.: Handwriting segmentation of constrained Oriya text. Sadhna 31, Part 6, 755–769 (2006)CrossRefGoogle Scholar
  4. 4.
    Rajashekararadhya, S.V., Vanaja Ranjan, P.: A Novel Zone Based Feature Extraction Algorithm for Handwritten Num Recognition of Four Indian Scripts. Digital Technology Journal 2, 41–51 (2009) ISSN 1802-5811 Google Scholar
  5. 5.
    Heutte, L., Paquet, T., et al.: A structural statistical feature based vector for handwritten character recognition. Pattern Recognition Letters 19, 629–641 (1998)CrossRefGoogle Scholar
  6. 6.
    Wang, X., Ding, X., Liu, C.: Gabor filters-based feature extraction for character recognition. Pattern Recognition Society (2004)Google Scholar
  7. 7.
    Pal, U., Roy, P.P.: Multi-oriented and curved text lines extraction from Indian documents. IEEE Trans. on Systems, Man and Cybernetics-Part B 34, 1676–1684 (2004)CrossRefGoogle Scholar
  8. 8.
    Mohanty, S., Behera, H.K.: A complete OCR Development System for Oriya Script. In: Proceedings of SIMPLE 2004, IIT Kharagpur (2004)Google Scholar
  9. 9.
    Pal, U., Wakabayashi, T., Kimura, F.: A System for Off-line Oriya Handwritten Character Recognition using Curvature Feature. IEEE (2007) 0-7695-3068-0/07Google Scholar
  10. 10.
    Ren, J.: Multi-order Standard Deviation Based Distance Metrics and its Application in Handwritten Chinese Character Recognition. In: 18th International conference on Pattern Recognition (ICPR 2006). IEEE (2006)Google Scholar
  11. 11.
    Liu, H., Ding, X.: Handwritten Character Recognition Using Gradient Feature and Quadratic Classifier with Multiple Discrimination Schemes. In: Proceedings of the 2005 Eighth International Conference on Document Analysis and Recognition (ICDAR 2005). IEEE (2005)Google Scholar
  12. 12.
    Blumenstein, M., Verma, B., Basli, H.: A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, ICDAR 2003. IEEE (2003) 0-7695-1960-1/03Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of MCAPurushottam Institute of Engineering & TechnologyRourkelaIndia
  2. 2.Department of CAITER, SOA UniversityBhubaneswarIndia

Personalised recommendations