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A novel form detection and removal scheme for document images

机译:一种新颖的文件图像形式检测与去除方案

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In this work, we propose a novel algorithm to extract form structure from form documents. First, an effective image binarization method is proposed as a preprocessor for the purpose of color quantization, which is able to deal with complex images by adaptively taking the global and local image characteristics into account. Then, a form feature extraction unit modeled by Markov random field (MRF) is followed to extract the form feature. It estimates the probability of each pixel from its neighborhood state, and recognizes the form structure based on the probability result. Unlike the geometric-based method in literature, our probability-based form feature extraction algorithm is more effective and can successfully recognize any size of form cells. In the experiment, our method has been integrated into a digital archiving system to evaluate its effectiveness in terms of both the visual and f-measurement numerical performance.
机译:在这项工作中,我们提出了一种从表单文档中提取表单结构的新颖算法。首先,提出了一种有效的图像二值化方法作为用于色彩量化的预处理器,该方法能够通过自适应地考虑全局和局部图像特征来处理复杂图像。然后,跟随由马尔可夫随机场(MRF)建模的表单特征提取单元以提取表单特征。它从其邻域状态估计每个像素的概率,并根据概率结果识别表单结构。与文献中基于几何的方法不同,我们基于概率的表单特征提取算法更有效,并且可以成功识别任何大小的表单单元。在实验中,我们的方法已集成到数字归档系统中,以从视觉和f测量数值性能两方面评估其有效性。

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