Advertisement

Enhancement and Cleaning of Handwritten Data by Using Neural Networks

  • José Luis Hidalgo
  • Salvador España
  • María José Castro
  • José Alberto Pérez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)

Abstract

In this work, artificial neural networks are used to clean and enhance scanned images for a handwritten recognition task. Multilayer perceptrons are trained in a supervised way using a set of simulated noisy images together with the corresponding clean images for the desired output. The neural network acquires the function of a desired enhancing method. The performance of this method has been evaluated for both noisy artificial and natural images. Objective and subjective methods of evaluation have shown a superior performance of the proposed method over other conventional enhancing and cleaning filters.

Keywords

handwritten recognition form processing image enhancement image denoising artificial neural networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: A comprehensive survey. IEEE Trans. on PAMI 22, 63–84 (2000)Google Scholar
  2. 2.
    Bozinovic, R.M., Srihari, S.N.: Off-Line Cursive Script Word Recognition. IEEE Trans. on PAMI 11, 68–83 (1989)Google Scholar
  3. 3.
    Bunke, H.: Recognition of Cursive Roman Handwriting – Past, Present and Future. In: 7th International Conference on Document Analysis and Recognition (ICDAR 2003), Edinburgh, Scotland (2003)Google Scholar
  4. 4.
    Toselli, A.H., et al.: Integrated Handwriting Recognition and Interpretation using Finite-State Models. International Journal of Pattern Recognition and Artificial Intelligence 18, 519–539 (2004)CrossRefGoogle Scholar
  5. 5.
    Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks – a review. Pattern Recognition 35, 2279–2301 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    España, S., Castro, M.J., Hidalgo, J.L.: The SPARTACUS-Database: a Spanish Sentence Database for Offline Handwriting Recognition. In: IV International conference on Language Resources and Evaluation (LREC 2004), Lisbon, Portugal, pp. 227–230 (2004)Google Scholar
  7. 7.
    Guyon, I., Haralick, R.M., Hull, J.J., Philips, I.T.: Data Sets for OCR and Document Image Understanding Research. In: Bunke, H., Wang, P.S.P. (eds.) Handbook of Character Recognition and Document Image Analysis, pp. 779–799. World Scientific, Singapore (1997)Google Scholar
  8. 8.
    Hull, J.J.: A database for handwritten text recognition research. IEEE Trans. on PAMI 16, 550–554 (1994)Google Scholar
  9. 9.
    Marti, U.V., Bunke, H.: The IAM-database: an English sentence adatabase for offline handwriting recognition. International Journal on Document Analysis and Recognition 5, 39–46 (2002)zbMATHCrossRefGoogle Scholar
  10. 10.
    Suen, C.Y., et al.: Computer recognition of unconstrained handwritten numerals. Special Issue of Proc. IEEE 7, 1162–1180 (1992)Google Scholar
  11. 11.
    Viard-Gaudin, C., et al.: The IRESTE On/Off (IRONOFF) Dual Handwriting Database. In: Fifth International Conference on Document Analysis and Recognition, Bangalore, India, pp. 455–458 (1999)Google Scholar
  12. 12.
    Wilkinson, R., et al.: The first census optical character recognition systems conference. In: #NISTIR 4912, The U.S. Bureau of Census and the National Institute of Standards and Technology, Gaithersburg, MD (1992)Google Scholar
  13. 13.
    Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley Publishing Company, Reading (1993)Google Scholar
  14. 14.
    Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, New York (1982)zbMATHGoogle Scholar
  15. 15.
    Serra, J.: Image Analysis and Mathematical Morphology, vol. 2. Academic Press, New York (1988)Google Scholar
  16. 16.
    Holzmann, G.: Beyond Photography. Prentice Hall Professional Technical Reference (1988)Google Scholar
  17. 17.
    Suzuki, K., Horiba, I., Sugie, N.: Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images. IEEE Trans. on PAMI 25, 1582–1596 (2003)Google Scholar
  18. 18.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  19. 19.
    Trier, Ø.D., Jain, A.K.: Goal-Directed Evaluation of Binarization Methods. IEEE Trans. on PAMI 17, 1191–1201 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • José Luis Hidalgo
    • 1
  • Salvador España
    • 1
  • María José Castro
    • 1
  • José Alberto Pérez
    • 2
  1. 1.Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de ValenciaValenciaSpain
  2. 2.Departamento de Informática de Sistemas y ComputadoresUniversidad Politécnica de ValenciaValenciaSpain

Personalised recommendations