Review: Document parsing in AWS, Azure, and Google Cloud

Nancy J. Delong

Data have been composed for thousands of many years, in numerous scripts and on numerous media. Clay tablets, stone tablets, wax tablets, papyrus, parchment, and paper all preceded digital media. In our hurry to go from paper to digital media, the most prevalent shortcut has been to scan paper into PDF files, which have the advantage of remaining digital and portable, but the drawback of remaining effectively unstructured.

What providers have to have as they streamline their functions is structured info, but getting from unstructured to structured files has been time-consuming. There have been numerous solutions and providers available for OCR (optical character recognition) and text mining, without having there remaining an total dominant player in the subject. To fully grasp the size of the problem, consider that eighty% to 90% of info is now unstructured, and the quantity of unstructured info is expanding from tens of zettabytes to hundreds of zettabytes. (A person zettabyte is just one billion terabytes.)

The typical technique to parsing a PDF doc entails segmenting each individual site, implementing OCR (often completed employing convolutional neural networks), figuring out the structure, extracting the text of curiosity, and changing digits to numeric values. Some providers can just take the next actions as effectively, extracting entities and inferring sentiment from picked text fields, these kinds of as content, opinions, and reviews.

In this posting we’ll examine the doc parsing and splitting providers out there from the major a few community cloud vendors: AWS, Microsoft Azure, and Google Cloud. The use conditions these providers address consist of extracting text and tagged values from lending and procurement files, contracts, driver’s licenses, and passports.

AWS doc parsers

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