Niblack Binarization on Document Images: Area Efficient, Low Cost, and Noise Tolerant Stochastic Architecture

摘要

Binarization plays a crucial role in Optical Character Recognition (OCR) ancillary domains, such as recovery of degraded document images. In Document Image Analysis (DIA), selecting threshold is not trivial since it differs from one problem (dataset) to another. Instead of trying several different thresholds for one dataset to another, we consider noise inherency of document images in our proposed binarization scheme. The proposed stochastic architecture implements the local thresholding technique: Niblack's binarization algorithm. We introduce a stochastic comparator circuit that works on unipolar stochastic numbers. Unlike the conventional stochastic circuit, it is simple and easy to deploy. We implemented it on the Xilinx Virtex6 XC6VLX760-2FF1760 FPGA platform and received encouraging experimental results. The complete set of results are available upon request. Besides, compared to conventional designs, the proposed stochastic implementation is better in terms of time complexity as well as fault-tolerant capacity.