On Multifont Character Classification in Telugu

  • Venkat Rasagna
  • K. J. Jinesh
  • C. V. Jawahar
Part of the Communications in Computer and Information Science book series (CCIS, volume 139)

Abstract

A major requirement in the design of robust OCRs is the invariance of feature extraction scheme with the popular fonts used in the print. Many statistical and structural features have been tried for character classification in the past. In this paper, we get motivated by the recent successes in object category recognition literature and use a spatial extension of the histogram of oriented gradients (HOG) for character classification. Our experiments are conducted on 1453950 Telugu character samples in 359 classes and 15 fonts. On this data set, we obtain an accuracy of 96-98% with an SVM classifier.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sankar, K.P., Ambati, V., Pratha, L., Jawahar, C.V.: Digitizing a million books: Challenges for document analysis. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 425–436. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Neeba, N.V., Jawahar, C.V.: Empirical evaluation of character classification schemes. In: Seventh International Conference on Advances in Pattern Recognition (ICAPR), pp. 310–313. IEEE, Los Alamitos (2009)Google Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893. IEEE, Los Alamitos (2005)Google Scholar
  4. 4.
    Negi, A., Bhagvati, C., Krishna, B.: An OCR system for telugu. In: Proceedings of Sixth International Conference on Document Analysis and Recognition (ICDAR), pp. 1110–1114. IEEE, Los Alamitos (2002)Google Scholar
  5. 5.
    Jawahar, C.V., Kumar, P., Kiran, R., et al.: A blingual OCR for hindi-telugu documents and its applications. In: Proceedings of Seventh International Conference on Document Analysis and Recognition (ICDAR), pp. 408–412. IEEE, Los Alamitos (2003)Google Scholar
  6. 6.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2169–2178. IEEE, Los Alamitos (2006)Google Scholar
  7. 7.
    Maji, S., Malik, J.: Fast and accurate digit classification. Technical Report UCB/EECS-2009-159, EECS Department, University of California, Berkeley (2009)Google Scholar
  8. 8.
    De Campos, T.E., Babu, B.R., Varma, M.: Character recognition in natural images. In: Proceedings of International Conference on Computer Vision Theory and Applications (VISAPP), INSTICC, pp. 273–280 (2009)Google Scholar
  9. 9.
    Ilayaraja, P., Neeba, N.V., Jawahar, C.V.: Efficient implementation of SVM for large class problems. In: 19th International Conference on Pattern Recognition (ICPR), pp. 1–4. IEEE, Los Alamitos (2009)Google Scholar
  10. 10.
    Maji, S., Berg, A.C., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE, Los Alamitos (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Venkat Rasagna
    • 1
  • K. J. Jinesh
    • 1
  • C. V. Jawahar
    • 1
  1. 1.International Institute of Information TechnologyHyderabadIndia

Personalised recommendations