[HN Gopher] Battle of document info extraction services: GCP vs....
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       Battle of document info extraction services: GCP vs. AWS vs. Azure
        
       Author : ctk_brian
       Score  : 38 points
       Date   : 2021-04-22 18:40 UTC (4 hours ago)
        
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 (TXT) w3m dump (www.crosstab.io)
        
       | ajcp wrote:
       | Very interesting review, thanks for the read! If I may, I've had
       | some different experiences.
       | 
       | 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...
        
       | tims33 wrote:
       | There is so much potential to these technologies, but even
       | autogenerated documents have so many embedded semantics that it
       | is hard to train these tools for a wide variety of document
       | formats.
        
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       (page generated 2021-04-22 23:01 UTC)