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Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction

  • Alicia Fornés
  • Sergio Escalera
  • Josep LLadós
  • Gemma Sánchez
  • Petia Radeva
  • Oriol Pujol
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)

Abstract

One of the major difficulties of handwriting recognition is the variability among symbols because of the different writer styles. In this paper we introduce the boosting of blurred shape models with error correction, which is a robust approach for describing and recognizing handwritten symbols tolerant to this variability. A symbol is described by a probability density function of blurred shape model that encodes the probability of pixel densities of image regions. Then, to learn the most distinctive features among symbol classes, boosting techniques are used to maximize the separability among the blurred shape models. Finally, the set of binary boosting classifiers is embedded in the framework of Error Correcting Output Codes (ECOC). Our approach has been evaluated in two benchmarking scenarios consisting of handwritten symbols. Compared with state-of-the-art descriptors, our method shows higher tolerance to the irregular deformations induced by handwritten strokes.

Keywords

Zernike Moment Multiclass Problem Symbol Recognition Handwritten Document Shape Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 8(2), 337–374 (1998)MathSciNetGoogle Scholar
  2. 2.
    Dietterich, T., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Artificial Intelligence Research 2, 263–286 (1995)zbMATHGoogle Scholar
  3. 3.
    Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol Recognition: Current Advances and Perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–127. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Torralba, A., Murphy, K., Freeman, W.: Sharing visual features for multiclass and multiview object detection. Technical Report, Massachusetts Institute of Technology Computer Science and Artificial Intelligence, MIT AIM (2004)Google Scholar
  5. 5.
    Escalera, S., Pujol, O., Radeva, P.: ECOC-ONE: A Novel Coding and Decoding Strategy. In: International Conference on Pattern Recognition (ICPR), Hong Kong, vol. 3, pp. 578–581 (2006)Google Scholar
  6. 6.
    Fornés, A., Lladós, J., Sánchez, G.: Primitive segmentation in old handwritten music scores. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 279–290. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)zbMATHCrossRefGoogle Scholar
  8. 8.
    Pujol, O., Radeva, P., Vitrià, J.: Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 1007–1012 (2006)CrossRefGoogle Scholar
  9. 9.
    Manjunath, B., Salembier, P., Sikora, T.: Introduction to mpeg-7. In: Multimedia content description interface, John Wiley and Sons, Chichester (2002)Google Scholar
  10. 10.
    Kim, W.: A new region-based shape descriptor. Technical report, Hanyang University and Konan Technology (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Alicia Fornés
    • 1
  • Sergio Escalera
    • 1
  • Josep LLadós
    • 1
  • Gemma Sánchez
    • 1
  • Petia Radeva
    • 1
  • Oriol Pujol
    • 1
  1. 1.Computer Vision Center, Dept. of Computer Science, Universitat Autònoma de Barcelona, 08193, BellaterraSpain

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