Publication: Investigating the Power of Integral Histograms for Document Images, a Binarization Case Study
Authors: Anguelos Nicolaou, Marcus Liwicki, Rolf Ingolf
Published in: Graphics Recognition. Current Trends and Challenges (GREC 2013 post-proceedings)
In this paper we introduce the use of integral IH (histograms) for document analysis. IH take advantage of the great increase of the memory size available on computers over time. By storing selected histogram features into each pixel position, several image filters can be calculated within constant complexity. In other words, time complexity is remarkably reduced by using more memory. While IH received a lot of attention in the computer vision field, they have not been investigated for document analysis so far. As a first step into this direction, we analyze IH for the toy problem of image binarization which is a prerequisite for many graphics and text recognition systems. The results of our participation in the HDIBCO2010 competition as well as our experiments with all DIBCO datasets showing the capabilities of this novel method for Document Image analysis.
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