Efficient Multiscale Sauvola's Binarization

Authors

Guillaume Lazzara, Thierry Géraud

Journal

International Journal of Document Analysis and Recognition

Type

article

Date

2013-01-01

Abstract

This work is focused on the most commonly used binarization method: Sauvola's. It performs relatively well on classical documents. However, three main defects remain: the window parameter of Sauvola's formula do not fit automatically to the content; it is not robust to low contrasts; it is non-invariant with respect to contrast inversion. Thus, on documents such as magazines, the content may not be retrieved correctly which is crucial for indexing purpose. In this paper, we describe how to implement an efficient multiscale implementation of Sauvola's algorithm in order to guarantee good binarization for both small and large objects inside a single document without adjusting the window size to the content. We also describe on how to implement it in an efficient way, step by step. Pixel-based accuracy and OCR evaluations are performed on more than 120 documents. This implementation remains notably fast compared to the original algorithm. For fixed parameters, text recognition rates and binarization quality are equal or better than other methods on small and medium text and is significantly improved on large text. Thanks to the way it is implemented, it is also more robust on textured text and on image binarization. This implementation extends the robustness of Sauvola's algorithm by making the results almost insensible to the window size whatever the object sizes. Its properties make it usable in full document analysis toolchains.

Bibtex (lrde.bib)

@Article{	  lazzara.13.ijdar,
  author	= {Guillaume Lazzara and Thierry G\'eraud},
  title		= {Efficient Multiscale {S}auvola's Binarization},
  journal	= {International Journal of Document Analysis and
		  Recognition},
  year		= {2013},
  note		= {Accepted},
  abstract	= {This work is focused on the most commonly used
		  binarization method: Sauvola's. It performs relatively well
		  on classical documents. However, three main defects remain:
		  the window parameter of Sauvola's formula do not fit
		  automatically to the content; it is not robust to low
		  contrasts; it is non-invariant with respect to contrast
		  inversion. Thus, on documents such as magazines, the
		  content may not be retrieved correctly which is crucial for
		  indexing purpose. In this paper, we describe how to
		  implement an efficient multiscale implementation of
		  Sauvola's algorithm in order to guarantee good binarization
		  for both small and large objects inside a single document
		  without adjusting the window size to the content. We also
		  describe on how to implement it in an efficient way, step
		  by step. Pixel-based accuracy and OCR evaluations are
		  performed on more than 120 documents. This implementation
		  remains notably fast compared to the original algorithm.
		  For fixed parameters, text recognition rates and
		  binarization quality are equal or better than other methods
		  on small and medium text and is significantly improved on
		  large text. Thanks to the way it is implemented, it is also
		  more robust on textured text and on image binarization.
		  This implementation extends the robustness of Sauvola's
		  algorithm by making the results almost insensible to the
		  window size whatever the object sizes. Its properties make
		  it usable in full document analysis toolchains.}
}