Battle of document info extraction services: GCP vs. AWS vs. Azure

Since the outcome of that comparison is that nothing is really great, the best course of action would be "look further". :-)

(Speaking on behalf of one such service - Rossum.ai. We actually extracted data from a dataset in the same domain as the article, 25000 television ad invoices, in November with 94% dollar-weighed accuracy. It was a project together with eVentures data: https://rossum.ai/blog/presidential-campaign-spend-analysis/)

I work for one of the biggest supermarket chains in the US as part of the team implementing an invoice processing capability for the enterprise to utilize. We literally take in thousands of paper/non-digitized invoices a day, and in our testing have found Azure's Form Recognizer (AFR) to be very dependable and confidently accurate. I have also professionally used Google Form Parser and ABBYY's OCR engine, but not it's cloud offering.

> it's also the only service fast enough to be part of a synchronous pipeline.

I assume what you're talking about here is exposing the processing capability and response as part of a tool that is utilized by a person. While maybe as a one-off edge case, we've never seen the use in building for this. When talking about form processing the real goal of any enterprise is to get the invoice data into their system of record where it can be validated, addressed, and maintained. This does not require a "man-in-the-middle" approach wherein the user submits the invoice and then expects the results to be immediately returned so that they may...what, put them in the system of record, right? We've found that the "time to affect" workflow is the same regardless of whether it is hand-keyed or as the result of an AFR response to be programmatically submitted to the system.

> requires a custom model to be trained before extracting data

This is simply not true. AFR provides quite a few pre-built models[1] that we have found to return confidence scores consistently above 70%. To put that in perspective a human averages 66% accuracy when performing data-entry of this type[2]. Sure, they don't necessarily provide for invoice line items (which requires much more complex key-value arrays and matrices) they can be utilized to capture metadata on an invoice that can then inform on how and where it may be moved along in the "processing" flow.

We've also found that building a single, "monolithic [custom] model" able to address our specific vendor invoices with more finely tuned value returns has been fairly easy to build and maintain.

1. https://docs.microsoft.com/en-us/azure/cognitive-services/fo... 2. https://www.sciencedirect.com/science/article/abs/pii/S07475...

The cloud services reviewed here are typically components in a much larger end-to-end process. They are valuable because they are fast, and work at scale.

Suggesting that the technology is useless because it can't parse a random set of 51 invoices misses the actual use case for which these services are appropriate.