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Today, I would like to talk to you about how to understand TableBank. Many people may not know much about it. In order to make you understand better, the editor has summarized the following contents for you. I hope you can get something from this article.

TableBank: high quality callout table dataset

Although humans can easily judge a table visually, it is not easy for machines to judge what a table is and the relationship between the contents of the table because of its various layouts and styles. The traditional rule-based table recognition method requires a lot of manual operation in the background once changing a document, while the existing machine learning methods can not obtain a large number of effective tagging data, so it is difficult to support the application in the actual scene. As a result, TableBank came into being.

TableBank is a table detection and recognition data set, based on open, large-scale Word documents and LaTex documents, created by weak supervision methods. Different from the traditional weakly supervised training set, TableBank not only has high data quality, but also its data scale is several orders of magnitude larger than the previous manually marked table analysis data set, and the amount of table data has reached 417000.

However, in order for the machine to read the table, it is necessary to be able to identify what the table is from the document, and then to identify the information in the table area.

After reading the above, do you have any further understanding of how to understand TableBank? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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