Niblack algorithm for Document binarization

There are range of methods that can help in this situation:

1. Of course, you can change algorithm it self =)
2. Also it is possible just apply morphology filters: first you apply maximum in the window, and after - minimum. You should tune windows size to achieve a better result, see wiki.
3. You can choose the hardest but the better way and try to improve Niblack's scheme. It is necessary to increase Niblack's windows size if standard deviation is smaller than some fixed number (should be tuned).

Did you look here: https://stackoverflow.com/a/9891678/105037

local_mean = imfilter(X, filt, 'symmetric');
local_std = sqrt(imfilter(X .^ 2, filt, 'symmetric'));
X_bin = X >= (local_mean + k_threshold * local_std);

I don't see many options here if you insist to use niblack. You can change the size and type of the filter, and the threshold.

BTW, it seems that your original image has colors. This information can significantly improve black text detection.

i tried sauvola algorithm in this paper Adaptive document image binarization J. Sauvola*, M. PietikaKinen section 3.3

it's a modified version of niblack algorithm which uses a modified equation of niblack

which returned a pretty good answers :

as well as i tried another modification of Niblack which is implemented in this paper in the 5.5 Algorithm No. 9a: Université de Lyon, INSA, France (C. Wolf, J-M Jolion)

which returned a good results as well :