Directional Decomposition for Odia Character Recognition

  • Chandana Mitra
  • Arun K. Pujari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


Present work aims at analyzing the role of directional features for efficient recognition of printed Odia characters. The characteristics of Odia scripts that demand for separate rigorous OCR research are identified. Directional features are extracted by directional decomposition of character image and using fixed zoning. The zones are determined based on the input character patterns. Initial experiment with a modest size of 20 features are taken by considering 4 directions and 5 fixed zones yield very promising results. It is shown that these features yield nearly 95% recognition accuracy by multi-class SVM classifiers. High acuracy of recognition justifies the importance of directional decomposition which indirectly captures the stroke information of the script. In another experiment, we consider 164 dimensional feature vectors by taking zones for the entire image. The observation made here can prove to be useful in building an efficient OCR system for Odia characters.


OCR of Indic Scripts Feature extraction Directional decomposition Odia scripts 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bai, Z.-L., Huo, Q.: A study on the use of 8- Directional features for online handwritten character recognition. In: ICDAR 2005, pp. 262–266 (2005)Google Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at:
  3. 3.
    Chaudhuri, B.B., Pal, U., Mitra, M.: Automatic recognition of printed Oriya script. In: SADHANA, Part 1, vol. 27, pp. 23–34 (2002)Google Scholar
  4. 4.
    Fujisawa, H., Liu, C.-L.: Directional Pattern Matching for Character Recognition Revisited. In: ICDAR, pp. 794–798. IEEE Computer Society (2003)Google Scholar
  5. 5.
    Gao, X., Jin, L.-W., Yin, J.-X., Huang, J.-C.: A New Stroke-Based Directional Feature Extraction Approach for Handwritten Chinese Character Recognition. In: ICDAR 2010, p. 635 (2001)Google Scholar
  6. 6.
    Harit, G., Chaudhury, S., Garg, R.: GFG-Based Compression and Retrieval of Document Images in Indian Scripts. In: Govindraju, Setlu (eds.) Guide to OCR for Indic Scripts, Advances in Pattern Recognition, pp. 269–284 (2010)Google Scholar
  7. 7.
    Jin, L., Wei, G.: Handwritten Chinese character recognition with directional decomposition of cellular features. Jr of Circuit, System and Computers 8, 517–524 (1998)CrossRefGoogle Scholar
  8. 8.
    Negi, A., Bhagvati, C., Krishna, B.: An OCR system for Telugu. In: ICDAR 2001, pp. 1110–1114 (2001)Google Scholar
  9. 9.
    Pal, U., Chaudhuri, B.B.: Indian script character recognition-A survey. Pattern Recognition 37, 1887–1899 (2004)CrossRefGoogle Scholar
  10. 10.
    Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: OffLine Handwritten Character Recognition of Devanagari Script. In: Proc. 9th International Conference on Document Analysis and Recognition, pp. 496–500 (2007)Google Scholar
  11. 11.
    Pujari, A.K., Naidu, C.D., Jinga, B.C.: An adaptive and intelligent character recognizer for Telugu scripts using multiresolution analysis and associative measures. Image Vision Comput. 22(14), 1221–1227 (2002)CrossRefGoogle Scholar
  12. 12.
    Trier, O.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition-A survey. Pattern Recognition 29(4), 641–662 (1996)CrossRefGoogle Scholar
  13. 13.
    Yu, L., Wang, R.: Shape representation based on mathematical morphology. Pattern Recognition Letters 26, 1354–1362 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Chandana Mitra
    • 1
  • Arun K. Pujari
    • 2
  1. 1.Sambalpur University Institute of Information Technology (SUIIT)OdishaIndia
  2. 2.School of Computer & Information Sc.University of HyderabadHyderabadIndia

Personalised recommendations