Niblack algorithm for Document binarization

Question

i've this photo :

and i'm trying to make Document binarization using niblack algorithm i've implemented the simple Niblack algorithm

T = mean + K* standardDiviation

and that was it's result:

the problem is there's some parts of the image in which the window doesn't contain any objects so it detects the noise as objects and elaborates them .

i tried to apply blurring filter then global thresholding that was the result :

which wont be solved by any other filter i guess the only solution is preventing the algorithm from detecting global noise if the window i free from object

i'm interested to do this using niblack algorithm not using other algorithm so any suggestions ?

Betrothal

See also: liris.cnrs.fr/christian.wolf/software/binarize – Coeval

Answer 1

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 :

Betrothal

Answer 2

Did you look here: https://mcmap.net/q/906470/-niblack-thresholding

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.

Wimple

Answer 3

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).

Statist

Answer 4

i tried the niblack algorithm with k=-0.99 and windows=990 using optimisation:

Shafait – “Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images”, 2008

with : T = mean + K* standardDiviation; i have this result :

the implementation of algorithm is taken here

Hydrogenous