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.