We describe a threshold-based local algorithm for im- age binarization. The main idea is to compute a transition energy using pixel value differences taken from a neighbor- hood around the pixel of interest. By filtering the pixels with low positive and negative energy, we keep two subsets in the neighborhood, corresponding to higher positive and nega- tive energy values. The binarization threshold is calculated using a statistical model of the high energy pixels. Experi- ments show that this new approach is faster and better than current state-of-the-art algorithms.