ICDAR 2019 Competition on Table Detection and Recognition (cTDaR)
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The aim of this competition is to evaluate the performance of state of the art methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done. The Ground Truth is provided in a similar format as for the ICDAR 2013 competition (see [2]): <?xml version="1.0" encoding="UTF-8"?> <<strong>document</strong> filename='filename.jpg'> <<strong>table</strong> id='Table_1540517170416_3'> <strong> <Coords points="180,160 4354,160 4354,3287 180,3287"/></strong> <<strong>cell</strong> id='TableCell_1540517477147_58' <strong>start-row</strong>='0' <strong>start-col</strong>='0' <strong>end-row</strong>='1' <strong>end-col</strong>='2'> <<strong>Coords</strong> <strong>points</strong>="180,160 177,456 614,456 615,163"/> </cell> ... </table> ... </document> The difference to Gobel et al. [2] is the Coords tag which defines a table/cell as a polygon specified by a list of coordinates. For B.1 the table and its coordinates is given together with the input image. Important Note: For the modern dataset, the convex hull of the content describes a cell region. For the historical dataset, it is requested that the output region of a cell is the cell boundary. This is necessary due to the characteristics of handwritten text, which is often overlapping with different cells. See also: http://sac.founderit.com/tasks.html The evaluation tool is available at github: https://github.com/cndplab-founder/ctdar_measurement_tool
本次竞赛旨在评估当前最先进的表格检测（TRACK A）与表格识别（TRACK B）方法的性能。其中赛道A将提供包含一个或多个表格的文档图像。赛道B下设两个子赛道：子赛道B.1仅提供表格区域，因此仅需完成表格结构识别任务；子赛道B.2不提供任何先验信息，即需要同时完成表格区域检测与表格结构识别任务。 本次竞赛的真值标注（Ground Truth）采用与ICDAR 2013竞赛相似的格式（详见参考文献[2]），示例格式如下： <?xml version="1.0" encoding="UTF-8"?> <<strong>document</strong> filename='filename.jpg'> <<strong>table</strong> id='Table_1540517170416_3'> <strong><Coords points="180,160 4354,160 4354,3287 180,3287"/></strong> <<strong>cell</strong> id='TableCell_1540517477147_58' <strong>start-row</strong>='0' <strong>start-col</strong>='0' <strong>end-row</strong>='1' <strong>end-col</strong>='2'> <<strong>Coords</strong> <strong>points</strong>="180,160 177,456 614,456 615,163"/> </cell> ... </table> ... </document> 与Gobel等人[2]的差异在于，本次竞赛的Coords标签将表格/单元格定义为由坐标列表指定的多边形。 对于子赛道B.1，输入图像将附带表格及其坐标信息。 重要提示：针对现代化数据集，单元格区域以其内容的凸包（convex hull）进行描述；针对历史手写数据集，要求单元格输出区域为其真实边界。这一要求源于手写文本常与不同单元格重叠的特性。 相关参考链接：http://sac.founderit.com/tasks.html 评估工具可在GitHub获取：https://github.com/cndplab-founder/ctdar_measurement_tool
