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.

中文翻译：

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文档图像上的 Niblack 二值化：面积高效、低成本和耐噪声的随机架构

二值化在光学字符识别 (OCR) 辅助领域中起着至关重要的作用，例如恢复退化的文档图像。在文档图像分析 (DIA) 中，选择阈值并非易事，因为它因一个问题（数据集）而异。在我们提出的二值化方案中，我们考虑了文档图像的噪声固有性，而不是为一个数据集尝试几个不同的阈值。所提出的随机架构实现了局部阈值技术：Niblack 的二值化算法。我们介绍了一种适用于单极随机数的随机比较器电路。与传统的随机电路不同，它简单且易于部署。我们在 Xilinx Virtex6 XC6VLX760-2FF1760 FPGA 平台上实现了它，并收到了令人鼓舞的实验结果。可根据要求提供完整的结果集。此外，与传统设计相比，所提出的随机实现在时间复杂度和容错能力方面更好。