# An Ante Hoc Enhancement Method for Image-Based Complex Financial Table Extraction
## 5 Conclusions  
In this paper, we regard the problem of recognizing wrapped text in complex tables as a special semantic matching problem and propose the preprocessing method ACTE for complex financial table extraction. By utilizing an OCR model, an interaction-based \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Recall** & **Precision** & **F1** \\ \hline GrabTab & 90.77 & 90.29 & 90.53 \\ GrabTab + SM & 87.93 & 74.74 & 80.80 \\ GrabTab + SM + FM & 96.18 & 95.09 & 95.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the ablation experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Recall** & **Precision** & **F1** & **TEDS** \\ \hline PPstructureV2 & 62.64 & 61.93 & 62.29 & 70.14 \\ Table & 82.96 & 79.57 & 81.23 & 84.47 \\ GrabTab & 90.77 & 90.29 & 90.53 & 89.63 \\ PPstructureV2+ACTE & 78.17 & 75.23 & 76.67 & 73.86 \\ Table & & & & \\ Transformer + & 92.99 & 92.25 & 92.62 & 90.79 \\ ACTE & & & & \\ GrabTab+ACTE & 96.18 & 95.09 & 95.63 & 92.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the complex financial table extraction experiment. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **With Correct Demarcation Line** & **With Incorrect Demarcation Line** \\ \hline Successfully extracted & 95.65\% & 0.53\% \\ Unsuccessfully extracted & 0.89\% & 2.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of the four kinds of condition in the extraction results. language model, and heuristic algorithms, ACTE makes full use of both visual information and textual semantic information to obtain the true row demarcation lines of a complex table and add them to the image. The effectiveness of this ante hoc enhancement method for current TE methods is demonstrated on a complex financial table dataset. The application of ACTE can save a lot of time for data scientists, financial professionals, and researchers engaged in the process of information extraction from financial documents. One limitation of our research is that our dataset is relatively small and restricted to tables in financial statements due to the extremely high cost of manual data collection, processing, labeling, and evaluation. Nevertheless, the proposed framework could be applied to other documents and domains by fine-tuning the text semantic matching model using a corresponding corpus. Despite differences in contents and terminologies, tables in other documents exhibit structural patterns similar to those within our dataset; therefore, the overall framework and heuristic algorithms could still work. Another limitation is that this method may not be effective for open-domain complex table extraction as the vocabulary increases and the semantic relationship between text pairs becomes more diverse and complex, making it harder to predict whether they belong to the same text. Lastly, as shown in our experimental result, the precision of the demarcation lines generated by our method is the bottleneck for the final extraction results, with more than half of the incorrect predictions appearing in the headers of tables. Therefore, improving the ability of ACTE to deal with table headers is another important issue worthy of further study in the future. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15010370/s1](https://www.mdpi.com/article/10.3390/app15010370/s1)]([https://www.mdpi.com/article/10.3390/app15010370/s1](https://www.mdpi.com/article/10.3390/app15010370/s1)). Figure S1. An example of the complex financial table extraction experiment. Rows that are inconsistent with the ground truth in the extraction result are highlighted in red. (a) An image of a complex financial table. (b) The ground truth of the financial table. (c) The extraction result for the original image obtained using GrabTab of the original image, which is labeled as 1 true positive and 2 false positives. (d) The enhanced image obtained using ACTE.