CVL Ruling Database
收藏资源简介：
The CVL ruling dataset was synthetically generated to allow for comparing different ruling removal methods. It is based on the ICDAR 2013 Handwriting Segmentation database [1]. It was generated by synthetically adding four different ruling images resulting in a total of 600 test images. The pixel values are: 255 background 155 ruling 100 text 0 ruling and text (overlaping) For processing, a binary image must be generated which sets all pixels to 0 that are not 255. When evaluating, the line GT image can be found by setting all pixel having value 155 to one (e.g. linImg = img == 155). The text GT image can be extracted by setting all values below 155 to zero (e.g. txtImg = img < 155). Then, true positives (tp), false positives (fp) and false negatives (fn) are defined as: tp = result & linImg & !txtImg fp = result & !txtImg fn = !result & linImg & !txtImg The database ships with a Matlab that gives evaluation results if all images are already processed.
CVL 标尺数据集（CVL ruling dataset）专为对比各类标尺去除方法而合成构建。该数据集依托ICDAR 2013手写分割数据库[1]生成，通过合成嵌入四张不同的标尺图像，最终得到总计600张测试样本。其像素取值规则如下： 255：背景区域 155：标尺线条区域 100：文本区域 0：标尺与文本重叠区域 在预处理环节，需生成二值图像：将所有非255的像素值置为0。在模型评估阶段，可通过将所有像素值为155的位置设为1，提取得到线条真值（ground truth，GT）图像（例如：linImg = img == 155）。文本真值图像则可通过将所有小于155的像素值置0提取得到（例如：txtImg = img < 155）。 随后，真阳性（tp）、假阳性（fp）与假阴性（fn）的定义如下： tp = 结果图像 & linImg & !txtImg fp = 结果图像 & !txtImg fn = !结果图像 & linImg & !txtImg 该数据集附带一份Matlab脚本，可在所有图像完成处理后输出评估结果。
