This paper presents our solution for ICDAR 2021 competition on scientific literature parsing taskB: table recognition to HTML. In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment.Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm. PSENet [2] is used to detect each text line in the table image. For text linerecognition, our model is also built on MASTER. Finally, in the box assignment phase, we associatedthe text boxes detected by PSENet with the structure item reconstructed by table structure prediction,and fill the recognized content of the text line into the corresponding item. Our proposed methodachieves a 96.84% TEDS score on 9,115 validation samples in the development phase, and a 96.32%TEDS score on 9,064 samples in the final evaluation phase.

中文翻译：

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平安-VCGroup的ICDAR 2021科学文献解析竞赛解决方案B：HTML的表格识别

本文介绍了针对ICDAR 2021竞赛的科学文献解析任务B：HTML表格识别的解决方案。在我们的方法中，我们将表内容识别任务分为四个子任务：表结构识别，文本行检测，文本行识别和框分配。我们的表结构识别算法是基于MASTER [1]定制的，鲁棒的图像文本识别算法。PSENet [2]用于检测表格图像中的每个文本行。对于文本划线识别，我们的模型也基于MASTER建立。最后，在框分配阶段，我们将PSENet检测到的文本框与通过表结构预测重构的结构项相关联，并将识别出的文本行内容填充到相应的项中。我们提出的方法在9时可达到96.84％的TEDS分数