A combined approach for the binarization of handwritten document images

  • Authors:

  • K. Ntirogiannis;B. Gatos;I. Pratikakis

  • Affiliations:

  • Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimioupoli, Ilissia, GR-15784 Athens, Greece and Institute of Informatics and Telecommunica ...;Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", GR-15310 Agia Paraskevi, Athens, Greece;Department of Electrical and Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi, Greece

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2014

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Abstract

There are many challenges addressed in handwritten document image binarization, such as faint characters, bleed-through and large background ink stains. Usually, binarization methods cannot deal with all the degradation types effectively. Motivated by the low detection rate of faint characters in binarization of handwritten document images, a combination of a global and a local adaptive binarization method at connected component level is proposed that aims in an improved overall performance. Initially, background estimation is applied along with image normalization based on background compensation. Afterwards, global binarization is performed on the normalized image. In the binarized image very small components are discarded and representative characteristics of a document image such as the stroke width and the contrast are computed. Furthermore, local adaptive binarization is performed on the normalized image taking into account the aforementioned characteristics. Finally, the two binarization outputs are combined at connected component level. Our method achieves top performance after extensive testing on the DIBCO (Document Image Binarization Contest) series datasets which include a variety of degraded handwritten document images.