Binarization of texts with varying lighting conditions using fuzzy inclusion and entropy measures

Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

“…Since there is not some publicly free, specialized database of the form "Image im-Proper threshold t", we decided to initially set Otsu's threshold as our t. This would help us to evaluate the potential of the whole process and to see if there is any meaning in continuing with it. Of course, based on our results in [2,20,21], we believed that most probably we would obtain some pretty good results. Thus, we trained an ANFIS with 252 vectors of the form (s…”

“…What we present here is different from these methods and our technique directly derives from our algorithms in [2,20,21]. The binarization of the image is accomplished based solely on some fuzzy inclusion and entropy measurements (after our image is transformed into a fuzzy set) and we don't use any information regarding the histogram of our input.…”

“…After our research in [2,20,21], we were convinced of the connection between threshold t and s 1 , s 2 , s 3 , s 4 , e. Of course, this relation is not some kind of simple or linear mathematical function so it was only logical to try establishing it in a more automatic manner. The use of an ANFIS seemed very appropriate and we directed our efforts towards this goal.…”

“…(which is in fact the boundary condition regarding fuzzy implications as presented in [92]). These two functions measure the inclusion of a fuzzy set A into ∅ as larger than zero and this was important for our algorithms in [2,20,21] to work. The following function E 1 is their common respective entropy measure:…”

“…Then, in [20,21], we adapted this general method to a local level. There, we focused on documents of non-uniform illumination and our algorithm was adjusted to cases of this particular domain.…”