Table Detection, Information Extraction and Structuring using Deep Learning

The amount of data being collected is drastically increasing day-by-day with lots of applications, tools, and online platforms booming in the present technological era. To handle and access this humongous data productively, it's necessary to develop valuable information extraction tools. One of the sub-areas that's demanding attention in the Information Extraction field is the fetching and accessing of data from tabular forms. To explain this in a subtle way, imagine you have lots of paperwork and documents where you would be using tables, and using the same, you would like to manipulate data. Conventionally, you can copy them manually (onto a paper) or load them into excel sheets. However, with table extraction, no sooner have you sent tables as pictures to the computer than it extracts all the information and stacks them into a neat document. This saves an ample of time and is less erroneous. As discussed in the previous section, tables are used frequently to represent data in a clean format. We can see them so often across several areas, from organizing our work by structuring data across tables to storing huge assets of companies.