Tablext：一个结合神经网络和启发式的表格抽取器
Tablext: A Combined Neural Network And Heuristic Based Table Extractor
摘要：
今天可用的数据有很大一部分是在表格中找到的。因此，在进行数据挖掘时，需要使用自动化的表抽取来获得全面的结果。目前流行的表格提取方法很难充分提取具有机器可读文本和结构数据的表格。更糟糕的是，许多表没有机器可读的数据，例如保存为图像的表，这使得大多数提取方法完全无效。为了解决这些问题，一个新的，通用的格式表提取工具，Tablext，提出。该工具结合了计算机视觉技术和机器学习方法，可以高效地识别和提取表格中的数据。Tablext首先使用自定义卷积神经网络（CNN）来识别和分离所有潜在的表。通过将自定义CNN与YOLO目标检测网络相结合，优化了识别过程。然后，用计算机视觉方法识别每个表的高层结构。另一个CNN使用这种高级结构元数据来识别确切的细胞位置。作为最后一步，对每个单元格执行光学字符识别（OCR），以提取其内容，而无需机器可读的文本。这种多阶段算法允许神经网络专注于完成复杂的任务，同时让图像处理方法有效地完成简单的任务。这使得所提出的方法足够通用，可以处理大量的表，而不管它们的内部编码或布局复杂性如何。此外，它变得足够准确，可以在ICDAR 2013表格数据集上超越竞争对手的最先进表格提取器。
英文摘要：
A significant portion of the data available today is found within tables. Therefore, it is necessary to use automated table extraction to obtain thorough results when data-mining. Today's popular state-of-the-art methods for table extraction struggle to adequately extract tables with machine-readable text and structural data. To make matters worse, many tables do not have machine-readable data, such as tables saved as images, making most extraction methods completely ineffective. In order to address these issues, a novel, general format table extractor tool, Tablext, is proposed. This tool uses a combination of computer vision techniques and machine learning methods to efficiently and effectively identify and extract data from tables. Tablext begins by using a custom Convolutional Neural Network (CNN) to identify and separate all potential tables. The identification process is optimized by combining the custom CNN with the YOLO object detection network. Then, the high-level structure of each table is identified with computer vision methods. This high-level, structural meta-data is used by another CNN to identify exact cell locations. As a final step, Optical Characters Recognition (OCR) is performed on every individual cell to extract their content without needing machine-readable text. This multi-stage algorithm allows for the neural networks to focus on completing complex tasks, while letting image processing methods efficiently complete the simpler ones. This leads to the proposed approach to be general-purpose enough to handle a large batch of tables regardless of their internal encodings or their layout complexity. Additionally, it becomes accurate enough to outperform competing state-of-the-art table extractors on the ICDAR 2013 table dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
