A Fast Fault-Tolerant Architecture for Sauvola Local Image Thresholding Algorithm Using Stochastic Computing

Binarization plays an important role in document image processing, particularly in degraded document images. Among all local image thresholding algorithms, Sauvola has excellent binarization performance for degraded document images. However, this algorithm is computationally intensive and sensitive to the noises from the internal computational circuits. In this paper, we present a stochastic implementation of Sauvola algorithm. Our experimental results show that the stochastic implementation of Sauvola needs much less time and area and can tolerate more faults, while consuming less power in comparison with its conventional implementation.