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Document Ink Bleed-Through Removal with Two Hidden Markov Random Fields and a Single Observation Field

机译:具有两个隐藏的Markov随机字段和一个观察字段的文档墨水直通去除

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We present a new method for blind document bleed-through removal based on separate Markov Random Field (MRF) regularization for the recto and for the verso side, where separate priors are derived from the full graph. The segmentation algorithm is based on Bayesian Maximum a Posteriori (MAP) estimation. The advantages of this separate approach are the adaptation of the prior to the contents creation process (e.g., superimposing two handwritten pages), and the improvement of the estimation of the recto pixels through an estimation of the verso pixels covered by recto pixels; moreover, the formulation as a binary labeling problem with two hidden labels per pixels naturally leads to an efficient optimization method based on the minimum cut/maximum flow in a graph. The proposed method is evaluated on scanned document images from the 18th century, showing an improvement of character recognition results compared to other restoration methods.
机译:我们提出了一种新的盲文档渗漏去除的新方法,该方法基于独立的Markov随机字段(MRF)正则化和正反面的正则化,其中独立的先验是从完整图中导出的。分割算法基于贝叶斯最大后验(MAP)估计。这种单独方法的优点是适应内容创建过程之前的内容(例如,叠加两个手写页面),以及通过估计由直方像素覆盖的反方像素来改进直方像素的估计;此外,将公式表示为每个像素具有两个隐藏标签的二进制标签问题自然会导致基于图形中最小切割/最大流量的有效优化方法。在18世纪的扫描文档图像上对提出的方法进行了评估,与其他恢复方法相比,该方法显示了字符识别结果的改进。

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