This paper presents a new perspective of text area segmentation from document images using a novel adaptive thresholding for image enhancement. Using sliding windows, the texture of the enhanced image is matched with that of a fixed training template image containing the typed letters ‘dB.’ The affine-invariant, low-dimensional difference theoretic texture feature set is used for the texture measurement. The distance matrix is binarized using Otsu threshold, and the ‘0’ pixels indicate the text area. One primary contribution of this paper is the novel adaptive thresholding for document image enhancement prior to the extraction of texture cues. The proposed adaptive thresholding mimics the ability of the human eye to iteratively adjust to varying light intensities through iterative gamma correction followed by contrast stretching so that the text becomes well defined against the background clutter. The text blobs so segmented are binarized using Yanowitz and Bruckstein method of text binarization, and the results are applied for evaluation with respect to the ground-truth annotations. We tested our algorithm on the benchmark DIBCO 2009, 2010, 2011, 2012, 2013 document image datasets in comparison with the state of the art. The high precision–recall and F-score values establish the efficiency of our approach.

中文翻译：

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通过新颖的自适应阈值和使用纹理提示的模板匹配从文档图像中进行文本区域分割

本文提出了使用新型自适应阈值进行图像增强的文档图像文本区域分割的新视角。使用滑动窗口，增强图像的纹理与包含键入字母“ dB”的固定训练模板图像的纹理匹配。仿射不变的低维差异理论纹理特征集用于纹理测量。使用Otsu阈值对距离矩阵进行二值化处理，“ 0”像素表示文本区域。本文的主要贡献之一是在提取纹理提示之前用于文档图像增强的新型自适应阈值处理。拟议的自适应阈值技术模仿了人眼通过迭代伽玛校正然后进行对比度拉伸来迭代调整以适应变化的光强度的能力，从而使文本在背景混乱的情况下变得清晰。使用Yanowitz和Bruckstein文本二值化方法对这样分割的文本blob进行二值化，然后将结果应用于针对真实注释的评估。与最新技术相比，我们在基准DIBCO 2009、2010、2011、2012、2013文档图像数据集上测试了我们的算法。高精度–调用和 并将结果用于地面真相注释的评估。与最新技术相比，我们在基准DIBCO 2009、2010、2011、2012、2013文档图像数据集上测试了我们的算法。高精度–调用和 并将结果用于地面真相注释的评估。与最新技术相比，我们在基准DIBCO 2009、2010、2011、2012、2013文档图像数据集上测试了我们的算法。高精度–调用和F分数值确定了我们方法的效率。