Abstract
In this paper we introduce the use of integral histograms (IH) 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 much attention in the computer vision field, they have not been intensively 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 show the capabilities of this novel method for Document Image analysis.
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Notes
- 1.
As made available http://liris.cnrs.fr/christian.wolf/software/binarize/
- 2.
A binary version of our benchmarking program http://nicolaou.homouniversalis.org/demos/2013/05/15/Binarization-Demo.html
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Acknowledgment
This work has been supported by the Swiss National Science Foundation with the HisDoc 2.0 project 205120 150173.
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Nicolaou, A., Ingold, R., Liwicki, M. (2014). Binarization with the Local Otsu Filter. In: Lamiroy, B., Ogier, JM. (eds) Graphics Recognition. Current Trends and Challenges. GREC 2013. Lecture Notes in Computer Science(), vol 8746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44854-0_14
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