  A novel method to convert color/multi-spectral images to gray-level images is
introduced to increase the performance of document binarization methods. The
method uses the distribution of the pixel data of the input document image in a
color space to find a transformation, called the dual transform, which balances
the amount of information on all color channels. Furthermore, in order to
reduce the intensity variations on the gray output, a color reduction
preprocessing step is applied. Then, a channel is selected as the gray value
representation of the document image based on the homogeneity criterion on the
text regions. In this way, the proposed method can provide a
luminance-independent contrast enhancement. The performance of the method is
evaluated against various images from two databases, the ICDAR'03 Robust
Reading, the KAIST and the DIBCO'09 datasets, subjectively and objectively with
promising results. The ground truth images for the images from the ICDAR'03
Robust Reading dataset have been created manually by the authors.
