# An Ante Hoc Enhancement Method for Image-Based Complex Financial Table Extraction
###### Abstract  
In the field of finance, the table is a common form of data organization. Extracting data from these tables in large quantities is a fundamental and important task for researchers. However, this can be a challenging task, as many tables exist in unstructured forms, such as scanned images in PDFs, rather than forms which can be easily processed, such as Excel spreadsheets. In recent years, a large number of table extraction methods utilizing heuristic algorithms or deep learning models have been proposed to free people from manual processing tasks, which are time-consuming and troublesome. Although existing methods achieve high levels of accuracy in processing some kinds of tables, they often fail to achieve optimal results when extracting complex financial tables with multi-line text and missing demarcation lines. In this article, we propose an enhancement method for image-based complex table extraction. This method consists of two modules: a split module and a filter module. The split module uses an OCR (optical character recognition) model to locate text regions, and a heuristic algorithm to obtain candidate demarcation lines. The filter module is based on a text semantic matching model and another heuristic algorithm. The experimental results show that the use of the proposed method can significantly improve the performance of different table extraction methods, with increases in F1 scores of between 5.10 and 14.36 points being recorded. table extraction; information extraction; text matching; artificial intelligence 2020 ac  
Traditionally, extracting tables from images has been a manual process. However, this method is inefficient, costly, and prone to errors. Therefore, the problem has arisen of how best to convert tables in unstructured forms, such as images, into structured forms, such as Excel spreadsheets or HTML sequences. This process is known as table extraction (TE), and involves three steps: table detection, table structure recognition, and table recovery. Table detection is the process of identifying and locating tables in an image. Table structure recognition involves identifying the structure of a table, which is the most important and challenging aspect of table extraction. Table recovery involves recognizing text and inserting it into the cells of the table. In recent years, extensive research has been conducted on table extraction, significantly improving the performance of table extraction models. In terms of table extraction, tables are classified into two categories: simple tables and complex tables. Simple tables are those with complete demarcation lines or those without spanning cells or cells with wrapped text (text that is split into several text lines). Complex tables are those with missing demarcation lines, spanning cells, or cells with wrapped text. The first techniques developed for table extraction were heuristic methods or simple machine learning methods [1; 2; 3; 4; 5; 6; 7]. These methods may yield good results in extracting simple tables, but they perform poorly when dealing with complex tables. In recent years, methods based on deep learning models [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] such as RCNN (region-based CNN) and FCNs (fully convolutional networks) have made significant progress in table extraction. However, their performance in extracting complex tables with wrapped text is still inadequate for practical use in specialized fields. For instance, in the table shown in Figure 1, the first cell in the second row has three text lines, but the line spaces within this cell are the same as those between cells, causing it to easily be recognized as three cells. To enhance the extraction of complex financial tables, it is essential to consider the semantic relationships between neighboring text lines and to provide the semantic information as an instruction for extraction methods to help them recognize those cells with wrapped text.