The task of table structure recognition is  to recognize the internal structure of a table , which is a key step to make machines understand tables . However, tabular data in unstructured digital documents, e.g. Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to complexity and diversity in their structure and style,  especially for complex tables. In this paper, we introduce Split, Embed and Merge (SEM), an accurate table structure recognizer.  In the first stage, we use the FCN to predict the potential regions of the table row (column) separators, so as to obtain the bounding boxes of the basic grids in the table. In the second stage, we not only extract the visual features corresponding to each grid through RoIAlign, but also use the off-the-shelf recognizer and the BERT to extract the semantic features . The fused features of both are used to characterize each table grid . We find that by adding additional semantic features to each grid, the ambiguity problem of the table structure from the visual perspective can be solved to a certain extent and achieve higher precision . Finally, we process the merging of these basic  grids in a self-regression manner. The correspondent merging results is learned by the attention maps in attention mechanism. With the proposed method, we can recognize the structure of tables well, even for complex tables. SEM can achieve an average F-Measure of 96.9%DIFDELCMD < \\%%% %DIF < on the SciTSR dataset which outperforms other methods by a large margin. Extensive experiments on other publicly available table structure recognition datasets show that our model achieves state-of-the-art.\end{abstract} 
Table structure recognition is an essential part for making machines understand tables. Its main task is to recognize the internal structure of a table . However, due to the complexity and diversity in their structure and style, it is very difficult to parse the tabular data into the structured format which machines can understand easily, especially for complex tables. In this paper, we introduce Split, Embed and Merge (SEM), an accurate table structure recognizer. Our model takes table images as input and can correctly recognize the structure of tables, whether they are simple or a complex tables. SEM is mainly composed of three parts, splitter, embedder and merger. In the first stage, we apply the splitter to predict the potential regions of the table row (column) separators, and obtain the fine grid structure of the table. In the second stage, by taking a full consideration of the textual information in the table, we fuse the output features for each table grid from both vision and language modalities. Moreover, we achieve a higher precision in our experiments through adding additional semantic features . Finally, we process the merging of these basic table grids in a self-regression manner. The correspondent merging results is learned %DIFDELCMD < \\%%% %DIF < on the SciTSR dataset which outperforms other methods by a large margin. Extensive experiments on other publicly available table structure recognition datasets show that our model achieves state-of-the-art.\end{abstract} through the attention mechanism. In our experiments, SEM achieves an average F1-Measure of 97.11\% on the SciTSR dataset which outperforms other methods by a large margin. We also won the first place in the complex table and third place in all tables in ICDAR 2021 Competition on Scientific Literature Parsing, Task-B. Extensive experiments on other publicly available datasets demonstrate that our model achieves state-of-the-art.
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The task of table
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an essential part for making machines understand tables. Its main task is
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, which is a key step to make machines understand tables
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tabular data in unstructured digital documents, e.g. Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to
due to the
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it is very difficult to parse the tabular data into the structured format which machines can understand easily,
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Our model takes table images as input and can correctly recognize the structure of tables, whether they are simple or a complex tables. SEM is mainly composed of three parts, splitter, embedder and merger.
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use the FCN
apply the splitter
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so as to obtain the bounding boxes of the basic grids in the
and obtain the fine grid structure of the
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we not only extract the visual features corresponding to each grid through RoIAlign, but also use the off-the-shelf recognizer and the BERT to extract the semantic features . The fused features of both are used to characterize
by taking a full consideration of the textual information in the table, we fuse the output features for
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. We find that by adding additional semantic features to each grid, the ambiguity problem of the table structure from the visual perspective can be solved to a certain extent and achieve higher precision
from both vision and language modalities. Moreover, we achieve a higher precision in our experiments through adding additional semantic features
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table
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by the attention maps in attention mechanism. With the proposed method, we can recognize the structure of tables well, even for complex tables. SEM can achieve an average F-Measure of 96.9
A
through the attention mechanism. In our experiments, SEM achieves an average F1-Measure of 97.11\% on the SciTSR dataset which outperforms other methods by a large margin. We also won the first place in the complex table and third place in all tables in ICDAR 2021 Competition on Scientific Literature Parsing, Task-B. Extensive experiments on other publicly available datasets demonstrate that our model achieves state-of-the-art.