A non-parametric binarization method based on ensemble of clustering algorithms

Binarization of document images still attracts the researchers especially when degraded document images are considered. This is evident from the recent Document Image Binarization Competition (DIBCO 2019) where we can see researchers from all over the world participated in this competition. In this paper, we present a novel binarization technique which is found to be capable of handling almost all types of degradations without any parameter tuning. Present method is based on an ensemble of three classical clustering algorithms (Fuzzy C-means, K-medoids and K-means++) to group the pixels as foreground or background, after application of a coherent image normalization method. It has been tested on four publicly available datasets, used in DIBCO series, 2016, 2017, 2018 and 2019. Present method gives promising results for the aforementioned datasets. In addition, this method is the winner of DIBCO 2019 competition.

Automated summary

Researchers are still interested in binarization of document images, especially when dealing with degraded images. A novel binarization technique capable of handling various degradations without parameter tuning has been proposed and won the DIBCO 2019 competition.