This paper presents a new algorithm for fusioning images of text-documents taken with different exposures.It is compared to several standard block oriented exposure- and focus-blending-algorithms.The recognition rate of a publicly available OCR-engine is used as a benchmark to quantify the results.Experiments show in average an improvement in the recognition rate from 0.46 to 0.64 by employing exposure blending as preprocessing step to an OCR. The presented algorithm of blending high-pass filtered images instead of original images further increases the recognition rate to 0.95.

In this paper, a new binarization algorithm for degraded document images is proposed. The method is based on positive and negative pixel energies using the Laplacian of an image. After a filtering step and morphological operations our local contrast segmentation method is able to detecting connected components. The given approach is applied to cases of sophisticated, challenging documents and other application scenarios like whiteboard and chalk images.

We describe a threshold-based local algorithm for im- age binarization. The main idea is to compute a transition energy using pixel value differences taken from a neighbor- hood around the pixel of interest. By filtering the pixels with low positive and negative energy, we keep two subsets in the neighborhood, corresponding to higher positive and nega- tive energy values. The binarization threshold is calculated using a statistical model of the high energy pixels. Experi- ments show that this new approach is faster and better than current state-of-the-art algorithms.

This paper describes a new versatile algorithm for correcting nonlinear distortions, such as curvature of book pages, in camera based document processing. We introduce the idea of using local orientation features to interpolate a vector field from which a warping mesh is derived. Ultimately, the image is corrected by approximating the nonlinear distortion with multiple linear projections. Since the algorithm does not derive the mesh directly from text baselines it is robust over arbitrarily complex text layouts. We describe a baseline detector for extracting the required local orientation features. We also sketch a method for correcting nonlinear distortions of a document's vertical axis with our algorithm.